Thesis-Code-Github
Ryan Hildebrandt
5/2/2020
1 Document Setup & Libraries
options(width = 10)
library(formattable)
library(DataExplorer)
library(esquisse)
library(car)
library(MASS)
library(ggfortify)
library(rpivotTable)
library(readxl)
library(data.table)
library(reshape2)
library(DescTools)
library(emmeans)
library(jmv)
library(ggthemes)
library(wesanderson)
library(ggpubr)
library(kableExtra)
library(apaTables)
library(gridExtra)
library(xlsx)
library(gganimate)
library(psych)
library(tidyverse)
library(magrittr)
library(sjstats)
library(jtools)
library(ggstance)
library(caret)
library(rmarkdown)
library(pander)2 Functions
# changes Session.ID where ID was mis-entered or had to be changed
fix.id <- function(x) {
fixd <- case_when(
x == "pb0401" ~ "pilotb0402",
x == "pilotb0829" ~ "pb0830",
x == "pilota1017" ~ "nb1018",
!x %in% c("pb0401", "pilotb0829", "pilota1017") ~ x
)
return(tolower(fixd))
}
# Reading and formatting individual SuperLab files
read.sl <- function(files) {
the.data <- read_delim(files,
"\t",
escape_double = FALSE,
col_names = FALSE,
col_types = cols(X5 = col_double()),
na = "empty",
trim_ws = TRUE,
skip = 6
)
if (length(colnames(the.data)) == 33) {
the.data$X10 <- NULL
colnames(the.data) <- paste0(rep("X", 32), 1:32)
}
colnames(the.data) <- sl.cols
data.frame(the.data)
}
# Return valence and word type of word
read.csv("./rawdata/Positive Emotion Label List.csv")## x
## 1 aroused
## 2 brave
## 3 cheer
## 4 delight
## 5 desire
## 6 devoted
## 7 ecstasy
## 8 elated
## 9 freedom
## 10 fun
## 11 happy
## 12 honest
## 13 hopeful
## 14 joy
## 15 kind
## 16 loyal
## 17 lust
## 18 pride
## 19 respect
## 20 securepos.list <- c(as.vector(read.csv("./rawdata/Positive Emotion Label List.csv")[, 1]), as.vector(read.csv("./rawdata/Positive Emotion Laden List.csv")[, 1]))
neg.list <- c(as.vector(read.csv("./rawdata/Negative Emotion Label List.csv")[, 1]), as.vector(read.csv("./rawdata/Negative Emotion Laden List.csv")[, 1]))
neu.list <- c(as.vector(read.csv("./rawdata/Neutral.A List.csv")[, 1]), as.vector(read.csv("./rawdata/Neutral.B List.csv")[, 1]))
e.list <- c(as.vector(read.csv("./rawdata/Positive Emotion Label List.csv")[, 1]), as.vector(read.csv("./rawdata/Negative Emotion Label List.csv")[, 1]))
el.list <- c(as.vector(read.csv("./rawdata/Positive Emotion Laden List.csv")[, 1]), as.vector(read.csv("./rawdata/Negative Emotion Laden List.csv")[, 1]))
get.val <- function(x) {
valence <- ifelse(x %in% pos.list, "Positive", ifelse(x %in% neg.list, "Negative", ifelse(x %in% neu.list, "Neutral", "None")))
return(valence)
}
get.wt <- function(x) {
wt <- ifelse(x %in% e.list, "Emotion-Label", ifelse(x %in% el.list, "Emotion-Laden", ifelse(x %in% neu.list, "Neutral", "None")))
return(wt)
}
# Remove trials which had all NRs due to Superlab error
remove.problem.trials <- function(x) {
trials.to.remove <- c("elated...comfort...puppy", "fun...kiss...victory", "joy...fun...cheer", "pride...jewel...tamper")
trimmed <- subset(x, !x$Trial.ID %in% trials.to.remove)
return(trimmed)
}
# Recode factors to numeric levels for aggregation
fac.to.num <- function(f) {
return(as.numeric(as.factor(f)))
}3 Import BDI & STAI Data
# Import inventory data and score----
# Reverse scored items
stai.rev <- c(23, 24, 27, 30, 32, 33, 37, 38, 41, 42, 43, 45, 48, 49, 52, 55, 56, 58, 61)
inventories <- read_excel("./rawdata/inventories.xlsx", col_types = c("text", rep("numeric", 61)))
inventories[, stai.rev] <- 5 - inventories[, stai.rev]
# Rename & fix ID discrepancies
# Score inventories and categorize based on cutoffs
bdi.cut <- 13
stai.cut <- 43
inventories <- inventories %>%
rename(., Session.ID = "id") %>%
mutate(.,
Session.ID = fix.id(.$Session.ID),
bdi.tot = rowSums(.[, 2:22]),
stai.y1.tot = rowSums(.[, 23:42]),
stai.y2.tot = rowSums(.[, 43:62])
) %>%
mutate(.,
"bdi" = ifelse(.$bdi.tot >= bdi.cut, "Scoring", "Non-Scoring"),
"stai.y1" = ifelse(.$stai.y1.tot >= stai.cut, "Scoring", "Non-Scoring"),
"stai.y2" = ifelse(.$stai.y2.tot >= stai.cut, "Scoring", "Non-Scoring")
) %>%
mutate(.,
mood.state.2 = ifelse(.$bdi == "Scoring" | .$stai.y1 == "Scoring" | .$stai.y2 == "Scoring", "Scoring", "Non-Scoring"),
mood.state.2.y1 = ifelse(.$bdi == "Scoring" | .$stai.y1 == "Scoring", "Scoring", "Non-Scoring"),
mood.state.2.y2 = ifelse(.$bdi == "Scoring" | .$stai.y2 == "Scoring", "Scoring", "Non-Scoring"),
mood.state.4.y1 = case_when(
.$bdi == "Scoring" & .$stai.y1 == "Non-Scoring" ~ "BDI Only",
.$bdi == "Non-Scoring" & .$stai.y1 == "Scoring" ~ "STAI Only",
.$bdi == "Scoring" & .$stai.y1 == "Scoring" ~ "BDI & STAI",
.$bdi == "Non-Scoring" & .$stai.y1 == "Non-Scoring" ~ "Non-Scoring"
),
mood.state.4.y2 = case_when(
.$bdi == "Scoring" & .$stai.y2 == "Non-Scoring" ~ "BDI Only",
.$bdi == "Non-Scoring" & .$stai.y2 == "Scoring" ~ "STAI Only",
.$bdi == "Scoring" & .$stai.y2 == "Scoring" ~ "BDI & STAI",
.$bdi == "Non-Scoring" & .$stai.y2 == "Non-Scoring" ~ "Non-Scoring"
),
mood.state.4 = case_when(
.$bdi == "Scoring" & (.$stai.y1 == "Non-Scoring" & .$stai.y2 == "Non-Scoring") ~ "BDI Only",
.$bdi == "Non-Scoring" & (.$stai.y1 == "Scoring" | .$stai.y2 == "Scoring") ~ "STAI Only",
.$bdi == "Scoring" & (.$stai.y1 == "Scoring" | .$stai.y2 == "Scoring") ~ "BDI & STAI",
.$bdi == "Non-Scoring" & (.$stai.y1 == "Non-Scoring" & .$stai.y2 == "Non-Scoring") ~ "Non-Scoring"
),
mood.state.8 = case_when(
.$bdi == "Scoring" & .$stai.y1 == "Non-Scoring" & .$stai.y2 == "Non-Scoring" ~ "BDI Only",
.$bdi == "Non-Scoring" & .$stai.y1 == "Scoring" & .$stai.y2 == "Non-Scoring" ~ "STAI Y1 Only",
.$bdi == "Non-Scoring" & .$stai.y1 == "Non-Scoring" & .$stai.y2 == "Scoring" ~ "STAI Y2 Only",
.$bdi == "Scoring" & .$stai.y1 == "Scoring" & .$stai.y2 == "Non-Scoring" ~ "BDI & STAI Y1",
.$bdi == "Scoring" & .$stai.y1 == "Non-Scoring" & .$stai.y2 == "Scoring" ~ "BDI & STAI Y2",
.$bdi == "Non-Scoring" & .$stai.y1 == "Scoring" & .$stai.y2 == "Scoring" ~ "STAI Y1 & STAI Y2",
.$bdi == "Scoring" & .$stai.y1 == "Scoring" & .$stai.y2 == "Scoring" ~ "BDI & STAI Y1 & STAI Y2",
.$bdi == "Non-Scoring" & .$stai.y1 == "Non-Scoring" & .$stai.y2 == "Non-Scoring" ~ "Non-Scoring"
)
)
# Get mood state counts & proportions
paged_table(inventories[, c(1, 63:75)])sapply(inventories[, 66:75], FUN = table)## $bdi
##
## Non-Scoring
## 48
## Scoring
## 23
##
## $stai.y1
##
## Non-Scoring
## 54
## Scoring
## 17
##
## $stai.y2
##
## Non-Scoring
## 42
## Scoring
## 29
##
## $mood.state.2
##
## Non-Scoring
## 31
## Scoring
## 40
##
## $mood.state.2.y1
##
## Non-Scoring
## 40
## Scoring
## 31
##
## $mood.state.2.y2
##
## Non-Scoring
## 36
## Scoring
## 35
##
## $mood.state.4.y1
##
## BDI & STAI
## 9
## BDI Only
## 14
## Non-Scoring
## 40
## STAI Only
## 8
##
## $mood.state.4.y2
##
## BDI & STAI
## 17
## BDI Only
## 6
## Non-Scoring
## 36
## STAI Only
## 12
##
## $mood.state.4
##
## BDI & STAI
## 17
## BDI Only
## 6
## Non-Scoring
## 31
## STAI Only
## 17
##
## $mood.state.8
##
## BDI & STAI Y1 & STAI Y2
## 9
## BDI & STAI Y2
## 8
## BDI Only
## 6
## Non-Scoring
## 31
## STAI Y1 & STAI Y2
## 3
## STAI Y1 Only
## 5
## STAI Y2 Only
## 9sapply(sapply(inventories[, 66:75], FUN = table), FUN = prop.table)## $bdi
##
## Non-Scoring
## 0.6760563
## Scoring
## 0.3239437
##
## $stai.y1
##
## Non-Scoring
## 0.7605634
## Scoring
## 0.2394366
##
## $stai.y2
##
## Non-Scoring
## 0.5915493
## Scoring
## 0.4084507
##
## $mood.state.2
##
## Non-Scoring
## 0.4366197
## Scoring
## 0.5633803
##
## $mood.state.2.y1
##
## Non-Scoring
## 0.5633803
## Scoring
## 0.4366197
##
## $mood.state.2.y2
##
## Non-Scoring
## 0.5070423
## Scoring
## 0.4929577
##
## $mood.state.4.y1
##
## BDI & STAI
## 0.1267606
## BDI Only
## 0.1971831
## Non-Scoring
## 0.5633803
## STAI Only
## 0.1126761
##
## $mood.state.4.y2
##
## BDI & STAI
## 0.23943662
## BDI Only
## 0.08450704
## Non-Scoring
## 0.50704225
## STAI Only
## 0.16901408
##
## $mood.state.4
##
## BDI & STAI
## 0.23943662
## BDI Only
## 0.08450704
## Non-Scoring
## 0.43661972
## STAI Only
## 0.23943662
##
## $mood.state.8
##
## BDI & STAI Y1 & STAI Y2
## 0.12676056
## BDI & STAI Y2
## 0.11267606
## BDI Only
## 0.08450704
## Non-Scoring
## 0.43661972
## STAI Y1 & STAI Y2
## 0.04225352
## STAI Y1 Only
## 0.07042254
## STAI Y2 Only
## 0.126760564 Import SAM Ratings
# Import ANEW ratings----
anew.elp.full <- read_xlsx("./rawdata/stimuli worksheet.xlsx",
sheet = 6,
col_names = TRUE,
col_types = c("text", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "numeric", "text"),
trim_ws = TRUE
)
anew.elp <- rename(anew.elp.full[, c(1, 2, 3, 4, 5, 6, 10, 13, 14)], Target = Word)
paged_table(anew.elp)# SAM lists----
pilot.sam.list <- as.vector(read.csv(file = "./rawdata/Pilot SAM List.txt", header = FALSE)[, 1])
pos.sam.list <- as.vector(read.csv(file = "./rawdata/Positive SAM List.txt", header = FALSE)[, 1])
neg.sam.list <- as.vector(read.csv(file = "./rawdata/Negative SAM List.txt", header = FALSE)[, 1])
pilot.sam.cols <- as.vector(rbind(paste(pilot.sam.list, rep("Valence", 80)), paste(pilot.sam.list, rep("Arousal", 80))))
pos.sam.cols <- as.vector(rbind(paste(pos.sam.list, rep("Valence", 80)), paste(pos.sam.list, rep("Arousal", 80))))
neg.sam.cols <- as.vector(rbind(paste(neg.sam.list, rep("Valence", 80)), paste(neg.sam.list, rep("Arousal", 80))))
# SAM raw data import----
qual.file <- "./rawdata/Qualtrics data.csv"
sam.raw <- read.csv(
file = qual.file,
header = TRUE, skip = 2, colClasses = c(
rep("character", 4),
rep("numeric", 2),
rep("character", 12),
rep("numeric", 160),
"character",
"numeric",
rep("character", 3),
rep("numeric", 160),
rep("numeric", 160)
),
col.names = as.vector(colnames(read.csv(
file = qual.file,
header = TRUE
))), na.strings = ""
)
# Reassigning some group names that recorded in a different form from the rest
sam.raw$Q1.7[sam.raw$Q1.7 == 3] <- "PA"
sam.raw$Q1.7[sam.raw$Q1.7 == 4] <- "PB"
# SAM ratings from PilotA and PilotB groups
sam.pilot <- sam.raw[sam.raw$Q1.7 == "Pilot A" | sam.raw$Q1.7 == "Pilot B", c(9, 18, (grep("Q7", colnames(sam.raw))), grep("Q2", colnames(sam.raw)))]
colnames(sam.pilot) <- c("QID", "Group", "PCode", "Age", "Gender", "Handedness", "DHOH", pilot.sam.cols)
sam.pilot[, 8:167] <- 10 - sam.pilot[, 8:167]
# SAM ratings from PA and PB groups
sam.pos <- sam.raw[sam.raw$Q1.7 == "PA" | sam.raw$Q1.7 == "PB", c(9, 18, (grep("Q7", colnames(sam.raw))), grep("Q3", colnames(sam.raw)))]
colnames(sam.pos) <- c("QID", "Group", "PCode", "Age", "Gender", "Handedness", "DHOH", pos.sam.cols)
sam.pos[, 8:167] <- 10 - sam.pos[, 8:167]
# SAM ratings from NA and NB groups
sam.neg <- sam.raw[sam.raw$Q1.7 == "NA" | sam.raw$Q1.7 == "NB", c(9, 18, (grep("Q7", colnames(sam.raw))), grep("Q4", colnames(sam.raw)))]
colnames(sam.neg) <- c("QID", "Group", "PCode", "Age", "Gender", "Handedness", "DHOH", neg.sam.cols)
sam.neg[, 8:167] <- 10 - sam.neg[, 8:167]
# Combined SAM rating table with all groups
sam <- bind_rows(sam.pilot, sam.pos, sam.neg)
# Fix column names and demographic responses
f <- c("female", "Female", "Woman")
m <- c("male", "Male")
dhoh <- c("hard of hearing", "Deaf")
ndhoh <- c("n/a", "Neither", "no", "No", "NO", "hearing", "hearing ", "None", "none")
sam <- sam %>%
rename(., Session.ID = PCode, Hearing.Status = DHOH) %>%
mutate(.,
Session.ID = fix.id(.$Session.ID),
Gender = ifelse(sam$Gender %in% m, "Male", ifelse(sam$Gender %in% f, "Female", "Non-Binary")),
Hearing.Status = ifelse(sam$DHOH %in% dhoh, "Deaf/Hard-of-Hearing", "Hearing")
)
paged_table(sam)5 Import SuperLab Data
# Read column names----
sl.cols <- gsub("NA", "", paste(
read.delim(
file = "./rawdata/timingtest2.txt", nrows = 2, skip = 3,
header = FALSE, na.strings = "", colClasses = rep("character", 32)
)[1, ],
read.delim(
file = "./rawdata/timingtest2.txt", nrows = 2, skip = 3,
header = FALSE, na.strings = "", colClasses = rep("character", 32)
)[2, ]
))[c(1:9, 11:33)]
# define column classes, list of superlab files, and session IDs
sl.class <- c(rep("character", 4), "numeric", "character", "numeric", "character", "numeric", "numeric", rep("character", 8), "numeric", rep("character", 14))
sl.files <- list.files("./rawdata")[grep("\\d{4}\\.txt", list.files("./rawdata"))] %>% paste0("./rawdata/",.)
sl.names <- gsub(".txt", "", sl.files)
# Read in all files and combine into a single table
sl.l <- lapply(sl.files, FUN = read.sl)
sl.raw <- bind_rows(sl.l)[c(1:11, 16:18, 25:32)]
paged_table(sl.raw)6 SuperLab Data Formatting and Variable Creation
sl.base <- sl.raw %>%
mutate(., # Fix some participant group names and session IDs that were mis-input
Participant.Group = ifelse(.$Participant.Group == "NA", "n_a", ifelse(.$Participant.Group == "NB", "n_b", tolower(.$Participant.Group))),
Session.ID = fix.id(.$Session.ID),
Participant.Name = .$Session.ID
) %>%
filter(
., # Remove unnecessary RT entries
.$Event.Name != "Fixation",
.$Event.Name != "Fixation Cross 1s7",
.$Event.Name != "Start Screen"
) %>%
mutate(., # Extract words from each trial and assign them to respective place in trial (target, pair1, pair2)
Word = substr(gsub("^.*?, ", "", .$Event.Name), 0, nchar(gsub("^.*?, ", "", .$Event.Name)) - 1)
) %>%
mutate(
Target = lag(.$Word, 2),
Pair1 = lag(.$Word),
Pair2 = .$Word
) %>%
mutate(., # Get unique ID for each trial and valence/wt for all stimuli words
Trial.ID = paste0(.$Target, "...", .$Pair1, "...", .$Pair2),
Target.V = get.val(.$Target),
Pair1.V = get.val(.$Pair1),
Pair2.V = get.val(.$Pair2),
Target.WT = get.wt(.$Target),
Pair1.WT = get.wt(.$Pair1),
Pair2.WT = get.wt(.$Pair2)
) %>%
mutate(., # Get valence and WT of stimuli from previous trial
Previous.Target.V = lag(.$Target.V),
Previous.Pair1.V = lag(.$Pair1.V),
Previous.Pair2.V = lag(.$Pair2.V),
Previous.Target.WT = lag(.$Target.WT),
Previous.Pair1.WT = lag(.$Pair1.WT),
Previous.Pair2.WT = lag(.$Pair2.WT)
) %>%
filter(
., # Remove unnecessary RT entries and DHOH participants
str_detect(.$Event.Name, "^Pair 2")
) %>%
mutate(., # Adjusting RTs based on SuperLab RTs starting from priming/satiation, not task
Reaction.Time = case_when(
.$X.Primed.Satiated == "Satiated" & .$Reaction.Time >= 25000 ~ .$Reaction.Time - (23830 + 1070),
.$X.Primed.Satiated == "Satiated" & .$Reaction.Time < 25000 ~ .$Reaction.Time,
.$X.Primed.Satiated == "Primed" & .$Reaction.Time >= 3000 ~ .$Reaction.Time - (3012 + 1070),
.$X.Primed.Satiated == "Primed" & .$Reaction.Time < 3000 ~ .$Reaction.Time
)
) %>%
mutate(., Reaction.Time = ifelse(.$Reaction.Time == 0, NA, .$Reaction.Time)) %>% # Recode 0s to NAs for no response trials
filter(., .$Reaction.Time >= 500 | is.na(.$Reaction.Time)) # Remove reaction times less than 500ms, leaving NAs for no responses
paged_table(sl.base)7 Merging SuperLab, SAM, and Inventory Data & Defining Pipeline to Format and Derive Variables from sl.merged
sl.merged <- sl.base %>%
merge(., select(inventories, c(Session.ID, bdi.tot:mood.state.8)), by = "Session.ID") %>% # merge with inventory data
merge(., select(sam, c(Session.ID:"scared Valence")), by = "Session.ID") %>% # merge with SAM data
filter(., .$Block.Name != "Practice") # remove all practice trials
sl.pipe <- function(sldf) {
sldf %>%
select(., c(Session.ID, Participant.Group, Block.Name, Participant.Response, Correct.Response, Error.Code, Reaction.Time, X.Primed.Satiated, Target:Hearing.Status)) %>% # keep the relevant columns
mutate(.,
RTCorrect = ifelse(.$Error.Code == "C", .$Reaction.Time, NA), # Only correct RT
RTError = ifelse(.$Error.Code == "E", .$Reaction.Time, NA), # only incorrect RT
Word.Type.n = ifelse(.$Target.WT == "Neutral", NA, .$Target.WT), # WT excluding neutral
Valence.n = ifelse(.$Target.V == "Neutral", NA, .$Target.V), # Valence excluding neutral
Pair.MM = ifelse(.$Pair1.V == .$Pair2.V, "Match", "Mismatch"), # valence relationship between pair1 and pair2
Pair.WT = case_when( # word type combination of each pair, including order
.$Pair1.WT == "Emotion-Label" & .$Pair2.WT == "Emotion-Label" ~ "Emotion-Label",
.$Pair1.WT == "Emotion-Laden" & .$Pair2.WT == "Emotion-Laden" ~ "Emotion-Laden",
.$Pair1.WT == "Neutral" & .$Pair2.WT == "Neutral" ~ "Neutral",
.$Pair1.WT == "Emotion-Label" & .$Pair2.WT == "Emotion-Laden" ~ "Mixed E.EL",
.$Pair1.WT == "Emotion-Laden" & .$Pair2.WT == "Emotion-Label" ~ "Mixed EL.E",
.$Pair1.WT == "Emotion-Label" & .$Pair2.WT == "Neutral" ~ "Mixed E.NEU",
.$Pair1.WT == "Neutral" & .$Pair2.WT == "Emotion-Label" ~ "Mixed NEU.E",
.$Pair1.WT == "Neutral" & .$Pair2.WT == "Emotion-Laden" ~ "Mixed NEU.EL",
.$Pair1.WT == "Emotion-Laden" & .$Pair2.WT == "Neutral" ~ "Mixed EL.NEU"
)
) %>%
mutate(.,
Correct.Response = ifelse(.$Pair1.V == .$Pair2.V, "Match", "Mismatch"),
) %>%
mutate(.,
Error.Code = ifelse(.$Correct.Response == .$Participant.Response, "C", ifelse(.$Participant.Response == "", "NR", "E"))
) %>%
mutate(.,
Pair.WT.n = ifelse(.$Pair.WT == "Neutral", NA, .$Pair.WT), # pair wt excluding neutral
TPRValence = case_when( # relationship between target and pair word valences
.$Target.V == .$Pair1.V & .$Target.V == .$Pair2.V ~ "Congruent",
.$Target.V == .$Pair1.V & .$Target.V != .$Pair2.V ~ "Pair 1 Congruent",
.$Target.V != .$Pair1.V & .$Target.V == .$Pair2.V ~ "Pair 2 Congruent",
.$Target.V != .$Pair1.V & .$Target.V != .$Pair2.V ~ "Incongruent"
),
TPRWT = case_when( # relationship between target and pair word word types
.$Target.WT == .$Pair1.WT & .$Target.WT == .$Pair2.WT ~ "Congruent",
.$Target.WT == .$Pair1.WT & .$Target.WT != .$Pair2.WT ~ "Pair 1 Congruent",
.$Target.WT != .$Pair1.WT & .$Target.WT == .$Pair2.WT ~ "Pair 2 Congruent",
.$Target.WT != .$Pair1.WT & .$Target.WT != .$Pair2.WT ~ "Incongruent"
)
) %>%
mutate(.,
Valence.Congruency = paste0(.$Target.V, ".", .$TPRValence), # code target valence and valence congruency as one variable
Word.Type.Congruency = paste0(.$Target.WT, ".", .$TPRWT), # code target wt and wt congruency as one variable
Primed.Satiated.MM = paste0(.$X.Primed.Satiated, ".", .$Pair.MM), # code priming/satiation and task match/mismatch together
RTC.CongruentV = ifelse(.$Error.Code == "C" & .$TPRValence %in% c("Congruent", "Pair 1 Congruent", "Pair 2 Congruent"), .$Reaction.Time, NA), # rts from valence congruent trials with correct responses
RTA.CongruentV = ifelse(.$TPRValence %in% c("Congruent", "Pair 1 Congruent", "Pair 2 Congruent"), .$Reaction.Time, NA) # all rts from valence congruent trials
) %>%
mutate(.,
Valence.Congruency.n = ifelse(.$Target.V == "Neutral", NA, .$Valence.Congruency), # valence congruency excluding neutral targets
Word.Type.Congruency.n = ifelse(.$Target.WT == "Neutral", NA, .$Word.Type.Congruency) # wt congruency excluding neutral targets
) %>%
mutate(., Valence.Congruency.Congruent = ifelse(.$TPRValence %in% c("Congruent", "Pair 1 Congruent", "Pair 2 Congruent"), .$Valence.Congruency, NA)) %>% # valence vongruency with only congruent trials
mutate(., Valence.Congruency.n.Congruent = ifelse(.$Target.WT == "Neutral", NA, .$Valence.Congruency.Congruent)) %>% # valence congruency, only congruent and excluding neutral targets
filter(., .$Hearing.Status == "Hearing") %>% # get rid of DHOH participants, they differed significantly from the rest in terms of mean RT
remove.problem.trials(.) %>% # get rid of misrecorded trials
return(.)
}8 SAM Descriptives
# Demographics Descriptives
formattable(describe(sam$Age))| vars | n | mean | sd | median | trimmed | mad | min | max | range | skew | kurtosis | se | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| X1 | 1 | 77 | 19.42857 | 1.922365 | 19 | 19.11111 | 1.4826 | 18 | 30 | 12 | 2.613894 | 10.33231 | 0.2190739 |
formattable(table(sam$Gender))##
## Female
## 45
## Male
## 31
## Non-Binary
## 1formattable(table(sam$Handedness))##
## Left-handed
## 8
## Right-handed
## 69formattable(table(sam$Hearing.Status))##
## Deaf/Hard-of-Hearing
## 2
## Hearing
## 75# SAM Valence & Arousal Descriptives, Comparisons & Plots
sam.desc <- describe(sam)
sam.desc.val <- sam.desc %>%
filter(., str_detect(rownames(.), "Valence$")) %>%
rename(., Target = "vars") %>%
mutate(., Target = gsub(" Valence", "", rownames(sam.desc)[str_detect(rownames(sam.desc), "Valence$")]))
sam.comp.val <- sam.desc.val %>%
merge(., select(anew.elp, c(Target, "Valence Mean", "Valence SD")), by = "Target") %>%
rename(., "Rated.Mean" = mean, "ANEW.Mean" = "Valence Mean", "ANEW.SD" = "Valence SD") %>%
mutate(., Difference = .$"Rated.Mean" - .$"ANEW.Mean")
plot_histogram(sam.comp.val$Difference)
sam.desc.ar <- sam.desc %>%
filter(., str_detect(rownames(.), "Arousal$")) %>%
rename(., Target = "vars") %>%
mutate(., Target = gsub(" Arousal", "", rownames(sam.desc)[str_detect(rownames(sam.desc), "Arousal$")]))
sam.comp.ar <- sam.desc.ar %>%
merge(., select(anew.elp, c(Target, "Arousal Mean", "Arousal SD")), by = "Target") %>%
rename(., "Rated.Mean" = mean, "ANEW.Mean" = "Arousal Mean", "ANEW.SD" = "Arousal SD") %>%
mutate(., Difference = .$"Rated.Mean" - .$"ANEW.Mean")
plot_histogram(sam.comp.ar$Difference)
paged_table(sam.desc)# arousal differences----
range(sam.comp.ar$"ANEW.Mean")## [1] 3.14
## [2] 8.17range(sam.comp.ar$"Rated.Mean")## [1] 3.114286
## [2] 7.523810range(sam.comp.ar$Difference)## [1] -3.153333
## [2] 1.188571ar.diffs <- subset(sam.comp.ar, abs(sam.comp.ar$Difference) > 2)
paged_table(ar.diffs)# collapse SAM ratings by participant----
sam.cat <- sam
sam.cat.col.val <- get.val((gsub(" Valence", "", colnames(sam.cat))[8:247]))
sam.cat$pos.val <- rowMeans(sam.cat[, 8:247][sam.cat.col.val == "Positive"])
sam.cat$neg.val <- rowMeans(sam.cat[, 8:247][sam.cat.col.val == "Negative"])
sam.cat$neu.val <- rowMeans(sam.cat[, 8:247][sam.cat.col.val == "Neutral"])
sam.cat <- select(sam.cat, c(Group:Hearing.Status, pos.val:neu.val))
#confirmatory word difference testing
t.test(sam.desc.ar$mean[get.val(sam.desc.ar$Target)=="Positive"],
sam.desc.ar$mean[get.val(sam.desc.ar$Target)=="Negative"])##
## Welch
## Two
## Sample
## t-test
##
## data: sam.desc.ar$mean[get.val(sam.desc.ar$Target) == "Positive"] and sam.desc.ar$mean[get.val(sam.desc.ar$Target) == "Negative"]
## t =
## 1.4274,
## df =
## 77.765,
## p-value
## = 0.1575
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.1350464 0.8190940
## sample estimates:
## mean of x
## 5.243452
## mean of y
## 4.901429t.test(sam.desc.ar$mean[get.val(sam.desc.ar$Target)=="Positive"],
sam.desc.ar$mean[get.val(sam.desc.ar$Target)=="Neutral"])##
## Welch
## Two
## Sample
## t-test
##
## data: sam.desc.ar$mean[get.val(sam.desc.ar$Target) == "Positive"] and sam.desc.ar$mean[get.val(sam.desc.ar$Target) == "Neutral"]
## t =
## 3.9478,
## df =
## 69.009,
## p-value
## =
## 0.0001875
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 0.3899289 1.1865862
## sample estimates:
## mean of x
## 5.243452
## mean of y
## 4.455195t.test(sam.desc.ar$mean[get.val(sam.desc.ar$Target)=="Neutral"],
sam.desc.ar$mean[get.val(sam.desc.ar$Target)=="Negative"])##
## Welch
## Two
## Sample
## t-test
##
## data: sam.desc.ar$mean[get.val(sam.desc.ar$Target) == "Neutral"] and sam.desc.ar$mean[get.val(sam.desc.ar$Target) == "Negative"]
## t =
## -2.1512,
## df =
## 66.874,
## p-value
## =
## 0.03507
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.86028181 -0.03218572
## sample estimates:
## mean of x
## 4.455195
## mean of y
## 4.901429t.test(sam.desc.val$mean[get.val(sam.desc.ar$Target)=="Positive"],
sam.desc.val$mean[get.val(sam.desc.ar$Target)=="Negative"])##
## Welch
## Two
## Sample
## t-test
##
## data: sam.desc.val$mean[get.val(sam.desc.ar$Target) == "Positive"] and sam.desc.val$mean[get.val(sam.desc.ar$Target) == "Negative"]
## t =
## 21.102,
## df =
## 75.767,
## p-value
## <
## 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 4.073098 4.922140
## sample estimates:
## mean of x
## 7.151190
## mean of y
## 2.653571t.test(sam.desc.val$mean[get.val(sam.desc.ar$Target)=="Positive"],
sam.desc.val$mean[get.val(sam.desc.ar$Target)=="Neutral"])##
## Welch
## Two
## Sample
## t-test
##
## data: sam.desc.val$mean[get.val(sam.desc.ar$Target) == "Positive"] and sam.desc.val$mean[get.val(sam.desc.ar$Target) == "Neutral"]
## t =
## 11.852,
## df =
## 77.446,
## p-value
## <
## 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 2.184459 3.066623
## sample estimates:
## mean of x
## 7.151190
## mean of y
## 4.525649t.test(sam.desc.val$mean[get.val(sam.desc.ar$Target)=="Neutral"],
sam.desc.val$mean[get.val(sam.desc.ar$Target)=="Negative"])##
## Welch
## Two
## Sample
## t-test
##
## data: sam.desc.val$mean[get.val(sam.desc.ar$Target) == "Neutral"] and sam.desc.val$mean[get.val(sam.desc.ar$Target) == "Negative"]
## t =
## 9.2145,
## df =
## 77.395,
## p-value
## =
## 4.431e-14
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 1.467554 2.276602
## sample estimates:
## mean of x
## 4.525649
## mean of y
## 2.6535719 SAM vs ANEW Categorical Comparisons
# Function to return the categorical valence of a given valence rating
get.val.cat <- function(x) {
valence <- ifelse(data.table::between(x, 6, 10, incbounds = FALSE),
"Positive",
ifelse(data.table::between(x, 0, 4, incbounds = FALSE),
"Negative",
ifelse(data.table::between(x, 4, 6, incbounds = TRUE),
"Neutral", "None"
)
)
)
return(valence)
}
# Comparing valence categories based on SAM averages
sam.cat.comp.val <- sam.comp.val %>%
mutate(.,
Rated.Category = get.val.cat(.$Rated.Mean),
ANEW.Category = get.val(.$Target),
Target.Word.Type = get.wt(.$Target)
) %>%
mutate(.,
Category.Comparison = ifelse(.$ANEW.Category == .$Rated.Category, "Same", "Different")
) %>%
mutate(.,
Rated.Word.Type = case_when(
.$ANEW.Category == "Neutral" & .$Rated.Category == "Negative" ~ "Emotion-Laden",
.$ANEW.Category == "Neutral" & .$Rated.Category == "Positive" ~ "Emotion-Laden",
.$ANEW.Category != "Neutral" & .$Rated.Category == "Neutral" ~ "Neutral",
TRUE ~ .$Target.Word.Type
)
)
paged_table(sam.cat.comp.val)# Isolate differences & Graph----
cat.comp.mismatches <- sam.cat.comp.val %>%
select(., c(Target:Rated.Mean, ANEW.Mean, Difference:Rated.Word.Type)) %>%
filter(., .$Category.Comparison == "Different")
paged_table(cat.comp.mismatches)# Mismatched words by category
unique(cat.comp.mismatches$Target[cat.comp.mismatches$ANEW.Category == "Positive" & cat.comp.mismatches$Rated.Category == "Neutral"])## [1] "baby"
## [2] "circus"unique(cat.comp.mismatches$Target[cat.comp.mismatches$ANEW.Category == "Negative" & cat.comp.mismatches$Rated.Category == "Neutral"])## [1] "alone"
## [2] "burial"
## [3] "tragedy"unique(cat.comp.mismatches$Target[cat.comp.mismatches$ANEW.Category == "Neutral" & cat.comp.mismatches$Rated.Category == "Negative"])## [1] "alley"
## [2] "beast"
## [3] "clumsy"
## [4] "cold"
## [5] "hide"
## [6] "hit"
## [7] "kick"
## [8] "noisy"
## [9] "obscene"
## [10] "rancid"
## [11] "revolt"
## [12] "rough"
## [13] "tamper"# Histograms
plot_histogram(cat.comp.mismatches$Difference)
plot_histogram(cat.comp.mismatches$Rated.Mean)
plot_histogram(cat.comp.mismatches$ANEW.Mean)
# Tables
formattable(table(cat.comp.mismatches$Rated.Category))##
## Negative
## 15
## Neutral
## 5formattable(table(cat.comp.mismatches$ANEW.Category))##
## Negative
## 3
## Neutral
## 13
## Positive
## 4formattable(table(cat.comp.mismatches$Target.Word.Type))##
## Emotion-Label
## 2
## Emotion-Laden
## 5
## Neutral
## 13formattable(table(cat.comp.mismatches$Rated.Word.Type))##
## Emotion-Label
## 1
## Emotion-Laden
## 14
## Neutral
## 5formattable(table(cat.comp.mismatches[, c(6, 7)]))## ANEW.Category
## Rated.Category Negative
## Negative 0
## Neutral 3
## ANEW.Category
## Rated.Category Neutral
## Negative 13
## Neutral 0
## ANEW.Category
## Rated.Category Positive
## Negative 2
## Neutral 2formattable(table(cat.comp.mismatches[, c(8, 10)]))## Rated.Word.Type
## Target.Word.Type Emotion-Label
## Emotion-Label 1
## Emotion-Laden 0
## Neutral 0
## Rated.Word.Type
## Target.Word.Type Emotion-Laden
## Emotion-Label 0
## Emotion-Laden 1
## Neutral 13
## Rated.Word.Type
## Target.Word.Type Neutral
## Emotion-Label 1
## Emotion-Laden 4
## Neutral 0# Comparing valence categories based individual ratings
sam.val.ind <- sam %>%
select(., Session.ID, ends_with("Valence")) %>%
melt(.) %>%
rename(.,
Target = "variable",
Rated.Mean = "value"
) %>%
mutate(.,
Target = gsub(" Valence", "", .$Target)
) %>%
mutate(.,
Rated.Category = get.val.cat(.$Rated.Mean),
ANEW.Category = get.val(.$Target),
Target.Word.Type = get.wt(.$Target)
) %>%
mutate(.,
Category.Comparison = ifelse(.$ANEW.Category == .$Rated.Category, "Same", "Different")
) %>%
mutate(.,
Rated.Word.Type = case_when(
.$ANEW.Category == "Neutral" & .$Rated.Category == "Negative" ~ "Emotion-Laden",
.$ANEW.Category == "Neutral" & .$Rated.Category == "Positive" ~ "Emotion-Laden",
.$ANEW.Category != "Neutral" & .$Rated.Category == "Neutral" ~ "Neutral",
TRUE ~ .$Target.Word.Type
)
)
paged_table(sam.val.ind)cat.ind.mismatches <- sam.val.ind %>%
filter(., .$Category.Comparison == "Different")
paged_table(cat.ind.mismatches)# Mismatched words by category
unique(cat.ind.mismatches$Target[cat.ind.mismatches$ANEW.Category == "Positive" & cat.ind.mismatches$Rated.Category == "Neutral"])## [1] "brave"
## [2] "snuggle"
## [3] "luxury"
## [4] "desire"
## [5] "cute"
## [6] "freedom"
## [7] "honest"
## [8] "jewel"
## [9] "victory"
## [10] "cheer"
## [11] "circus"
## [12] "fun"
## [13] "hopeful"
## [14] "cake"
## [15] "devoted"
## [16] "treat"
## [17] "aroused"
## [18] "secure"
## [19] "bride"
## [20] "diamond"
## [21] "ecstasy"
## [22] "baby"
## [23] "gift"
## [24] "kind"
## [25] "reward"
## [26] "wealthy"
## [27] "puppy"
## [28] "pride"
## [29] "loyal"
## [30] "comfort"
## [31] "respect"
## [32] "delight"
## [33] "hug"
## [34] "triumph"
## [35] "kiss"
## [36] "elated"
## [37] "lust"unique(cat.ind.mismatches$Target[cat.ind.mismatches$ANEW.Category == "Negative" & cat.ind.mismatches$Rated.Category == "Neutral"])## [1] "anger"
## [2] "failure"
## [3] "unhappy"
## [4] "misery"
## [5] "traitor"
## [6] "horror"
## [7] "rotten"
## [8] "death"
## [9] "danger"
## [10] "fear"
## [11] "tumor"
## [12] "tragedy"
## [13] "pain"
## [14] "victim"
## [15] "devil"
## [16] "annoy"
## [17] "hell"
## [18] "afraid"
## [19] "ache"
## [20] "alone"
## [21] "injury"
## [22] "poison"
## [23] "selfish"
## [24] "burial"
## [25] "scared"
## [26] "coffin"
## [27] "jail"
## [28] "hurt"
## [29] "abuse"
## [30] "cancer"
## [31] "divorce"
## [32] "mad"
## [33] "rage"
## [34] "lonely"
## [35] "rude"
## [36] "robber"
## [37] "crisis"
## [38] "grief"
## [39] "trauma"
## [40] "sad"unique(cat.ind.mismatches$Target[cat.ind.mismatches$ANEW.Category == "Neutral" & cat.ind.mismatches$Rated.Category == "Negative"])## [1] "hit"
## [2] "alien"
## [3] "cliff"
## [4] "rough"
## [5] "trumpet"
## [6] "obscene"
## [7] "body"
## [8] "alley"
## [9] "boxer"
## [10] "hide"
## [11] "vanity"
## [12] "reptile"
## [13] "hard"
## [14] "lion"
## [15] "defiant"
## [16] "cold"
## [17] "lump"
## [18] "revolt"
## [19] "dentist"
## [20] "tease"
## [21] "army"
## [22] "beast"
## [23] "clumsy"
## [24] "doctor"
## [25] "highway"
## [26] "hotel"
## [27] "kick"
## [28] "mystic"
## [29] "news"
## [30] "noisy"
## [31] "rancid"
## [32] "storm"
## [33] "swamp"
## [34] "tamper"
## [35] "tank"
## [36] "truck"
## [37] "vampire"
## [38] "volcano"# Tables
formattable(table(cat.ind.mismatches$Session.ID))##
## na0415
## 26
## na0416
## 26
## na0425
## 16
## na0903
## 24
## na0910
## 21
## na0917
## 36
## na0926
## 23
## na1003
## 24
## na1010
## 33
## na1017
## 19
## na1022
## 20
## na1023
## 26
## na1024
## 28
## na1028
## 28
## na1029
## 18
## na1030
## 29
## na1031
## 13
## nb0416
## 19
## nb0418
## 19
## nb0829
## 20
## nb0905
## 22
## nb0910
## 18
## nb0917
## 29
## nb1004
## 23
## nb1008
## 29
## nb1010
## 18
## nb1017
## 23
## nb1018
## 28
## nb1022
## 19
## nb1024
## 26
## nb1028
## 17
## nb1029
## 40
## nb1030
## 20
## nb1031
## 12
## nb1032
## 14
## pa0325
## 23
## pa0329
## 26
## pa0402
## 15
## pb0326
## 20
## pb0405
## 25
## pb0830
## 21
## pilota0325
## 32
## pilota0329
## 35
## pilota0402
## 32
## pilota0829
## 26
## pilota0905
## 30
## pilota0912
## 17
## pilota0919
## 27
## pilota1003
## 38
## pilota1008
## 29
## pilota1010
## 32
## pilota1022
## 29
## pilota1023
## 29
## pilota1024
## 34
## pilota1028
## 34
## pilota1029
## 33
## pilota1030
## 22
## pilota1031
## 30
## pilota1032
## 30
## pilotb0326
## 37
## pilotb0401
## 22
## pilotb0402
## 22
## pilotb0404
## 18
## pilotb0910
## 29
## pilotb0912
## 39
## pilotb0926
## 28
## pilotb1003
## 30
## pilotb1008
## 43
## pilotb1015
## 36
## pilotb1022
## 25
## pilotb1023
## 23
## pilotb1024
## 26
## pilotb1028
## 24
## pilotb1029
## 41
## pilotb1030
## 28
## pilotb1031
## 36
## pilotb1032
## 21formattable(table(cat.ind.mismatches$Target))##
## abuse
## 1
## ache
## 15
## afraid
## 6
## alien
## 8
## alley
## 37
## alone
## 31
## anger
## 5
## annoy
## 20
## army
## 24
## aroused
## 7
## baby
## 38
## beast
## 41
## body
## 7
## boxer
## 29
## brave
## 1
## bride
## 16
## burial
## 31
## cake
## 12
## cancer
## 3
## cheer
## 4
## circus
## 23
## cliff
## 15
## clumsy
## 31
## coffin
## 11
## cold
## 41
## comfort
## 2
## crisis
## 5
## cute
## 3
## danger
## 8
## death
## 1
## defiant
## 32
## delight
## 2
## dentist
## 24
## desire
## 9
## devil
## 21
## devoted
## 10
## diamond
## 17
## divorce
## 5
## doctor
## 27
## ecstasy
## 10
## elated
## 6
## failure
## 2
## fear
## 10
## freedom
## 4
## fun
## 26
## gift
## 6
## grief
## 1
## hard
## 45
## hell
## 13
## hide
## 46
## highway
## 11
## hit
## 55
## honest
## 7
## hopeful
## 8
## horror
## 8
## hotel
## 15
## hug
## 2
## hurt
## 6
## injury
## 7
## invest
## 26
## jail
## 12
## jewel
## 25
## joy
## 1
## kick
## 31
## kind
## 3
## kiss
## 36
## lion
## 22
## lonely
## 6
## loyal
## 37
## lump
## 38
## lust
## 21
## luxury
## 9
## mad
## 8
## misery
## 1
## mystic
## 27
## news
## 17
## noisy
## 37
## obscene
## 52
## pain
## 2
## poison
## 8
## pride
## 13
## puppy
## 5
## rage
## 8
## rancid
## 64
## reptile
## 18
## respect
## 7
## revolt
## 43
## reward
## 6
## robber
## 14
## rotten
## 9
## rough
## 37
## rude
## 12
## sad
## 7
## salute
## 25
## scared
## 3
## secure
## 11
## selfish
## 13
## snuggle
## 4
## storm
## 39
## swamp
## 22
## tamper
## 34
## tank
## 14
## tease
## 30
## tragedy
## 34
## traitor
## 9
## trauma
## 1
## treat
## 6
## triumph
## 3
## truck
## 7
## trumpet
## 70
## tumor
## 3
## unhappy
## 5
## vampire
## 27
## vanity
## 24
## victim
## 2
## victory
## 2
## volcano
## 31
## wealthy
## 11formattable(table(cat.ind.mismatches$Rated.Category))##
## Negative
## 897
## Neutral
## 710
## Positive
## 396formattable(table(cat.ind.mismatches$ANEW.Category))##
## Negative
## 367
## Neutral
## 1223
## Positive
## 413formattable(table(cat.ind.mismatches$Target.Word.Type))##
## Emotion-Label
## 349
## Emotion-Laden
## 431
## Neutral
## 1223formattable(table(cat.ind.mismatches$Rated.Word.Type))##
## Emotion-Label
## 30
## Emotion-Laden
## 1263
## Neutral
## 710formattable(table(cat.ind.mismatches[, c(4, 5)]))## ANEW.Category
## Rated.Category Negative
## Negative 0
## Neutral 357
## Positive 10
## ANEW.Category
## Rated.Category Neutral
## Negative 837
## Neutral 0
## Positive 386
## ANEW.Category
## Rated.Category Positive
## Negative 60
## Neutral 353
## Positive 0formattable(table(cat.ind.mismatches[, c(6, 8)]))## Rated.Word.Type
## Target.Word.Type Emotion-Label
## Emotion-Label 30
## Emotion-Laden 0
## Neutral 0
## Rated.Word.Type
## Target.Word.Type Emotion-Laden
## Emotion-Label 0
## Emotion-Laden 40
## Neutral 1223
## Rated.Word.Type
## Target.Word.Type Neutral
## Emotion-Label 319
## Emotion-Laden 391
## Neutral 010 Passing Data Through sl.pipe and Recoding Valence Categories
# Main Data Frame
sl.trimmed <- sl.pipe(sl.merged)
# get. functions for recoding
get.val.cat.avg <- function(x) {
valence <- sam.cat.comp.val$Rated.Category[sam.cat.comp.val$Target == x]
return(valence)
}
get.wt.avg <- function(x) {
wt <- sam.cat.comp.val$Rated.Word.Type[sam.cat.comp.val$Target == x]
return(wt)
}
get.val.cat.ind <- function(x, id) {
valence <- sam.val.ind$Rated.Category[sam.val.ind$Target == x & sam.val.ind$Session.ID == id]
return(valence)
}
get.wt.ind <- function(x, id) {
wt <- sam.val.ind$Rated.Word.Type[sam.val.ind$Target == x & sam.val.ind$Session.ID == id]
return(wt)
}
# Recoding Based on SAM Averages
sl.recoded.avg <- sl.trimmed %>%
mutate(., # rewrite stimuli valences and wts
Target.V = sapply(.$Target, FUN = get.val.cat.avg),
Pair1.V = sapply(.$Pair1, FUN = get.val.cat.avg),
Pair2.V = sapply(.$Pair2, FUN = get.val.cat.avg),
Target.WT = sapply(.$Target, FUN = get.wt.avg),
Pair1.WT = sapply(.$Pair1, FUN = get.wt.avg),
Pair2.WT = sapply(.$Pair2, FUN = get.wt.avg)
) %>%
sl.pipe(.)
# Recoding Based on Individual Participant Ratings
sl.recoded.ind <- sl.trimmed %>%
mutate(., # rewrite stimuli valences and wts
Target.V = mapply(get.val.cat.ind, .$Target, .$Session.ID),
Pair1.V = mapply(get.val.cat.ind, .$Pair1, .$Session.ID),
Pair2.V = mapply(get.val.cat.ind, .$Pair2, .$Session.ID),
Target.WT = mapply(get.wt.ind, .$Target, .$Session.ID),
Pair1.WT = mapply(get.wt.ind, .$Pair1, .$Session.ID),
Pair2.WT = mapply(get.wt.ind, .$Pair2, .$Session.ID)
) %>%
sl.pipe(.)
# Removing Based on SAM Averages
sl.removed.avg <- sl.trimmed %>%
filter(
.,
!.$Target %in% cat.comp.mismatches$Target,
!.$Pair1 %in% cat.comp.mismatches$Target,
!.$Pair2 %in% cat.comp.mismatches$Target
) %>%
sl.pipe(.)
nrow(sl.removed.avg)## [1] 3251# Removing Based on Individual Participant Ratings
sl.removed.ind <- sl.trimmed %>%
filter(
.,
get.val(.$Target) != mapply(get.val.cat.ind, .$Target, .$Session.ID),
get.val(.$Pair1) != mapply(get.val.cat.ind, .$Target, .$Session.ID),
get.val(.$Pair2) != mapply(get.val.cat.ind, .$Target, .$Session.ID)
) %>%
sl.pipe(.)
nrow(sl.removed.ind)## [1] 76811 Factorize & Aggregate For Repeated Measures Mixed ANOVAs
#Factorize
rmaov.pipe <- function(df) {
sl.factorized <- df %>%
mutate_if(sapply(., is.character), as.factor) %>%
mutate_if(sapply(., is.factor), fac.to.num)
sl.agg.desc <- sl.factorized %>%
dplyr::select(., Session.ID, Participant.Group, X.Primed.Satiated, Target.V, Target.WT, TPRValence, bdi, Error.Code, Reaction.Time) %>%
group_by(., Session.ID, Participant.Group, X.Primed.Satiated, Target.V, TPRValence, Target.WT, bdi, Error.Code) %>%
summarise_all(list(length = length, mean = mean, median = median, sd = sd, min = min, max = max)) %>%
rename(RTCMean = "mean", n = "length", Congruency = TPRValence)
sl.agg <- sl.factorized %>%
dplyr::select(., Session.ID, Participant.Group, X.Primed.Satiated, Target.V, Target.WT, TPRValence, bdi, Error.Code, Reaction.Time) %>%
group_by(., Session.ID, Participant.Group, X.Primed.Satiated, Target.V, TPRValence, Target.WT, bdi, Error.Code) %>%
summarise_all(list(mean = mean)) %>%
rename(RTCMean = mean, Congruency = TPRValence)
sl.agg.rm <- sl.factorized %>%
dplyr::select(Session.ID, Participant.Group, X.Primed.Satiated, Target.V, Target.WT, TPRValence, bdi, Error.Code, Reaction.Time) %>%
group_by(Session.ID, Participant.Group, X.Primed.Satiated, Target.V, TPRValence, Target.WT, bdi, Error.Code) %>%
summarise_all(list(mean = mean)) %>%
filter(Error.Code == 1, Target.WT != 3, Target.V != 3) %>%
merge(., c(distinct(sl.agg[, 1:2])), by = "Session.ID") %>%
dplyr::select(., -Participant.Group.y) %>%
rename(RTCMean = mean, Congruency = TPRValence, Participant.Group = Participant.Group.x) %>%
mutate(Participant.Group = ifelse(.$Participant.Group %in% c(1, 2), 1, 2))
sl.agg.rm.spss <- sl.agg.rm %>%
recast(.,
formula = Session.ID ~ Target.WT + X.Primed.Satiated,
id.var = c("Session.ID", "Target.WT", "X.Primed.Satiated"),
measure.var = "RTCMean",
fun = mean
) %>%
merge(., c(distinct(sl.agg[, c(1:2, 7)])), by = "Session.ID") %>%
mutate(Participant.Group = ifelse(.$Participant.Group %in% c(1, 2), 1, 2))
write.csv(sl.agg.rm.spss, "sl.agg.rm.spss.csv")
rm.simple <- jmv::anovaRM(
data = sl.agg.rm.spss,
rm = list(
list(
label="Word Type",
levels=c("Emotion-label", "Emotion-laden")),
list(
label="Primed/Satiated",
levels=c("Primed", "Satiated"))),
rmCells = list(
list(
measure="1_1",
cell=c("Emotion-label", "Primed")),
list(
measure="1_2",
cell=c("Emotion-label", "Satiated")),
list(
measure="2_1",
cell=c("Emotion-laden", "Primed")),
list(
measure="2_2",
cell=c("Emotion-laden", "Satiated"))),
bs = Participant.Group,
effectSize = c("ges", "eta", "partEta"),
depLabel = "RT",
rmTerms = ~ `Word Type` + `Primed/Satiated` + `Word Type`:`Primed/Satiated`,
bsTerms = ~ Participant.Group,
spherTests = TRUE,
leveneTest = TRUE,
postHoc = list(
"Word Type",
"Primed/Satiated",
c("Word Type", "Primed/Satiated"),
"Participant.Group",
c("Word Type", "Participant.Group"),
c(
"Primed/Satiated",
"Participant.Group"),
c(
"Word Type",
"Primed/Satiated",
"Participant.Group")),
postHocCorr = c("tukey","scheffe"),
emmPlots = FALSE,
emmWeights = FALSE,
groupSumm = TRUE)
rm.bdi <- jmv::anovaRM(
data = sl.agg.rm.spss,
rm = list(
list(
label="Word Type",
levels=c("Emotion-label", "Emotion-laden")),
list(
label="Primed/Satiated",
levels=c("Primed", "Satiated"))),
rmCells = list(
list(
measure="1_1",
cell=c("Emotion-label", "Primed")),
list(
measure="1_2",
cell=c("Emotion-label", "Satiated")),
list(
measure="2_1",
cell=c("Emotion-laden", "Primed")),
list(
measure="2_2",
cell=c("Emotion-laden", "Satiated"))),
bs = vars(Participant.Group, bdi),
effectSize = c("ges", "eta", "partEta"),
depLabel = "RT",
rmTerms = ~ `Word Type` + `Primed/Satiated` + `Word Type`:`Primed/Satiated`,
bsTerms = ~ Participant.Group + bdi + Participant.Group:bdi,
spherTests = TRUE,
leveneTest = TRUE,
postHoc = list(),
postHocCorr = NULL,
emmPlots = FALSE,
emmWeights = FALSE,
groupSumm = TRUE
)
return(list(
rm.simple,
rm.bdi
))
}
#rmaov.pipe(sl.trimmed)
#rmaov.pipe(sl.recoded.avg)
#rmaov.pipe(sl.recoded.ind)
#rmaov.pipe(sl.removed.avg)
#rmaov.pipe(sl.removed.ind)12 Complete SuperLab Descriptives
desc.pipe <- function(df) {
return(list(
prop.table(table(df$Participant.Group)),
prop.table(table(df$Correct.Response)),
prop.table(table(df$Error.Code)),
prop.table(table(df$X.Primed.Satiated)),
prop.table(table(df$Target.V)),
prop.table(table(df$Pair1.V)),
prop.table(table(df$Pair2.V)),
prop.table(table(df$Target.WT)),
prop.table(table(df$Pair1.WT)),
prop.table(table(df$Pair2.WT)),
prop.table(table(df$bdi)),
prop.table(table(df$stai.y1)),
prop.table(table(df$stai.y2)),
prop.table(table(df$mood.state.2)),
prop.table(table(df$mood.state.2.y1)),
prop.table(table(df$mood.state.2.y2)),
prop.table(table(df$mood.state.4.y1)),
prop.table(table(df$mood.state.4.y2)),
prop.table(table(df$mood.state.4)),
prop.table(table(df$mood.state.8)),
prop.table(table(df$Age)),
prop.table(table(df$Gender)),
prop.table(table(df$Handedness)),
prop.table(table(df$Hearing.Status)),
prop.table(table(df$Pair.MM)),
prop.table(table(df$Pair.WT)),
prop.table(table(df$TPRValence)),
prop.table(table(df$TPRWT)),
prop.table(table(df$Valence.Congruency)),
prop.table(table(df$Word.Type.Congruency)),
prop.table(table(df$Primed.Satiated.MM)),
describe(select(df, Reaction.Time, bdi.tot, stai.y1.tot, stai.y2.tot, Age))
))
}
desc.pipe(sl.trimmed)## [[1]]
##
## n_a
## 0.2493126
## n_b
## 0.2637947
## pilota
## 0.2507791
## pilotb
## 0.2361137
##
## [[2]]
##
## Match
## 0.5191567
## Mismatch
## 0.4808433
##
## [[3]]
##
## C
## 0.71384051
## E
## 0.26801100
## NR
## 0.01814849
##
## [[4]]
##
## Primed
## 0.500275
## Satiated
## 0.499725
##
## [[5]]
##
## Negative
## 0.3847846
## Neutral
## 0.2566453
## Positive
## 0.3585701
##
## [[6]]
##
## Negative
## 0.3270394
## Neutral
## 0.3846013
## Positive
## 0.2883593
##
## [[7]]
##
## Negative
## 0.3912007
## Neutral
## 0.3272227
## Positive
## 0.2815765
##
## [[8]]
##
## Emotion-Label
## 0.3715857
## Emotion-Laden
## 0.3717690
## Neutral
## 0.2566453
##
## [[9]]
##
## Emotion-Label
## 0.2887259
## Emotion-Laden
## 0.3266728
## Neutral
## 0.3846013
##
## [[10]]
##
## Emotion-Label
## 0.3268561
## Emotion-Laden
## 0.3459212
## Neutral
## 0.3272227
##
## [[11]]
##
## Non-Scoring
## 0.6736939
## Scoring
## 0.3263061
##
## [[12]]
##
## Non-Scoring
## 0.7587534
## Scoring
## 0.2412466
##
## [[13]]
##
## Non-Scoring
## 0.5871677
## Scoring
## 0.4128323
##
## [[14]]
##
## Non-Scoring
## 0.4295142
## Scoring
## 0.5704858
##
## [[15]]
##
## Non-Scoring
## 0.5593034
## Scoring
## 0.4406966
##
## [[16]]
##
## Non-Scoring
## 0.5013749
## Scoring
## 0.4986251
##
## [[17]]
##
## BDI & STAI
## 0.1268561
## BDI Only
## 0.1994500
## Non-Scoring
## 0.5593034
## STAI Only
## 0.1143905
##
## [[18]]
##
## BDI & STAI
## 0.24051329
## BDI Only
## 0.08579285
## Non-Scoring
## 0.50137489
## STAI Only
## 0.17231897
##
## [[19]]
##
## BDI & STAI
## 0.24051329
## BDI Only
## 0.08579285
## Non-Scoring
## 0.42951421
## STAI Only
## 0.24417965
##
## [[20]]
##
## BDI & STAI Y1 & STAI Y2
## 0.12685610
## BDI & STAI Y2
## 0.11365720
## BDI Only
## 0.08579285
## Non-Scoring
## 0.42951421
## STAI Y1 & STAI Y2
## 0.04252979
## STAI Y1 Only
## 0.07186068
## STAI Y2 Only
## 0.12978918
##
## [[21]]
##
## 18
## 0.43189734
## 19
## 0.24326306
## 20
## 0.11439047
## 21
## 0.11145738
## 22
## 0.07039413
## 23
## 0.01393217
## 25
## 0.01466544
##
## [[22]]
##
## Female
## 0.59816682
## Male
## 0.38790101
## Non-Binary
## 0.01393217
##
## [[23]]
##
## Left-handed
## 0.09899175
## Right-handed
## 0.90100825
##
## [[24]]
##
## Hearing
## 1
##
## [[25]]
##
## Match
## 0.5191567
## Mismatch
## 0.4808433
##
## [[26]]
##
## Emotion-Label
## 0.15398717
## Emotion-Laden
## 0.14060495
## Mixed E.EL
## 0.05132906
## Mixed E.NEU
## 0.08340972
## Mixed EL.E
## 0.08340972
## Mixed EL.NEU
## 0.10265811
## Mixed NEU.E
## 0.08945921
## Mixed NEU.EL
## 0.15398717
## Neutral
## 0.14115490
##
## [[27]]
##
## Congruent
## 0.3266728
## Incongruent
## 0.2438130
## Pair 1 Congruent
## 0.1860678
## Pair 2 Congruent
## 0.2434464
##
## [[28]]
##
## Congruent
## 0.1153071
## Incongruent
## 0.5769019
## Pair 1 Congruent
## 0.1475710
## Pair 2 Congruent
## 0.1602200
##
## [[29]]
##
## Negative.Congruent
## 0.17946838
## Negative.Incongruent
## 0.03849679
## Negative.Pair 1 Congruent
## 0.05774519
## Negative.Pair 2 Congruent
## 0.10907424
## Neutral.Congruent
## 0.03849679
## Neutral.Incongruent
## 0.14115490
## Neutral.Pair 1 Congruent
## 0.03849679
## Neutral.Pair 2 Congruent
## 0.03849679
## Positive.Congruent
## 0.10870761
## Positive.Incongruent
## 0.06416132
## Positive.Pair 1 Congruent
## 0.08982585
## Positive.Pair 2 Congruent
## 0.09587534
##
## [[30]]
##
## Emotion-Label.Congruent
## 0.03849679
## Emotion-Label.Incongruent
## 0.22419798
## Emotion-Label.Pair 1 Congruent
## 0.05132906
## Emotion-Label.Pair 2 Congruent
## 0.05756187
## Emotion-Laden.Congruent
## 0.03831347
## Emotion-Laden.Incongruent
## 0.21154904
## Emotion-Laden.Pair 1 Congruent
## 0.05774519
## Emotion-Laden.Pair 2 Congruent
## 0.06416132
## Neutral.Congruent
## 0.03849679
## Neutral.Incongruent
## 0.14115490
## Neutral.Pair 1 Congruent
## 0.03849679
## Neutral.Pair 2 Congruent
## 0.03849679
##
## [[31]]
##
## Primed.Match
## 0.2502291
## Primed.Mismatch
## 0.2500458
## Satiated.Match
## 0.2689276
## Satiated.Mismatch
## 0.2307974
##
## [[32]]
## vars
## Reaction.Time 1
## bdi.tot 2
## stai.y1.tot 3
## stai.y2.tot 4
## Age 5
## n
## Reaction.Time 5356
## bdi.tot 5455
## stai.y1.tot 5455
## stai.y2.tot 5455
## Age 5455
## mean
## Reaction.Time 1442.91
## bdi.tot 10.58
## stai.y1.tot 38.28
## stai.y2.tot 41.77
## Age 19.26
## sd
## Reaction.Time 471.22
## bdi.tot 8.39
## stai.y1.tot 9.04
## stai.y2.tot 10.18
## Age 1.52
## median
## Reaction.Time 1361.5
## bdi.tot 8.0
## stai.y1.tot 37.0
## stai.y2.tot 40.0
## Age 19.0
## trimmed
## Reaction.Time 1401.17
## bdi.tot 9.46
## stai.y1.tot 37.18
## stai.y2.tot 41.23
## Age 19.00
## mad
## Reaction.Time 457.38
## bdi.tot 7.41
## stai.y1.tot 5.93
## stai.y2.tot 10.38
## Age 1.48
## min
## Reaction.Time 539
## bdi.tot 0
## stai.y1.tot 24
## stai.y2.tot 22
## Age 18
## max
## Reaction.Time 2999
## bdi.tot 38
## stai.y1.tot 62
## stai.y2.tot 67
## Age 25
## range
## Reaction.Time 2460
## bdi.tot 38
## stai.y1.tot 38
## stai.y2.tot 45
## Age 7
## skew
## Reaction.Time 0.77
## bdi.tot 1.18
## stai.y1.tot 0.94
## stai.y2.tot 0.50
## Age 1.34
## kurtosis
## Reaction.Time 0.21
## bdi.tot 1.11
## stai.y1.tot 0.34
## stai.y2.tot -0.46
## Age 1.64
## se
## Reaction.Time 6.44
## bdi.tot 0.11
## stai.y1.tot 0.12
## stai.y2.tot 0.14
## Age 0.02desc.pipe(sl.recoded.avg)## [[1]]
##
## n_a
## 0.2493126
## n_b
## 0.2637947
## pilota
## 0.2507791
## pilotb
## 0.2361137
##
## [[2]]
##
## Match
## 0.4551787
## Mismatch
## 0.5448213
##
## [[3]]
##
## C
## 0.72318973
## E
## 0.25866178
## NR
## 0.01814849
##
## [[4]]
##
## Primed
## 0.500275
## Satiated
## 0.499725
##
## [[5]]
##
## Negative
## 0.4617782
## Neutral
## 0.2117324
## Positive
## 0.3264895
##
## [[6]]
##
## Negative
## 0.4168653
## Neutral
## 0.3204400
## Positive
## 0.2626948
##
## [[7]]
##
## Negative
## 0.5389551
## Neutral
## 0.2179652
## Positive
## 0.2430797
##
## [[8]]
##
## Emotion-Label
## 0.3587534
## Emotion-Laden
## 0.4295142
## Neutral
## 0.2117324
##
## [[9]]
##
## Emotion-Label
## 0.2758937
## Emotion-Laden
## 0.4036664
## Neutral
## 0.3204400
##
## [[10]]
##
## Emotion-Label
## 0.3204400
## Emotion-Laden
## 0.4615949
## Neutral
## 0.2179652
##
## [[11]]
##
## Non-Scoring
## 0.6736939
## Scoring
## 0.3263061
##
## [[12]]
##
## Non-Scoring
## 0.7587534
## Scoring
## 0.2412466
##
## [[13]]
##
## Non-Scoring
## 0.5871677
## Scoring
## 0.4128323
##
## [[14]]
##
## Non-Scoring
## 0.4295142
## Scoring
## 0.5704858
##
## [[15]]
##
## Non-Scoring
## 0.5593034
## Scoring
## 0.4406966
##
## [[16]]
##
## Non-Scoring
## 0.5013749
## Scoring
## 0.4986251
##
## [[17]]
##
## BDI & STAI
## 0.1268561
## BDI Only
## 0.1994500
## Non-Scoring
## 0.5593034
## STAI Only
## 0.1143905
##
## [[18]]
##
## BDI & STAI
## 0.24051329
## BDI Only
## 0.08579285
## Non-Scoring
## 0.50137489
## STAI Only
## 0.17231897
##
## [[19]]
##
## BDI & STAI
## 0.24051329
## BDI Only
## 0.08579285
## Non-Scoring
## 0.42951421
## STAI Only
## 0.24417965
##
## [[20]]
##
## BDI & STAI Y1 & STAI Y2
## 0.12685610
## BDI & STAI Y2
## 0.11365720
## BDI Only
## 0.08579285
## Non-Scoring
## 0.42951421
## STAI Y1 & STAI Y2
## 0.04252979
## STAI Y1 Only
## 0.07186068
## STAI Y2 Only
## 0.12978918
##
## [[21]]
##
## 18
## 0.43189734
## 19
## 0.24326306
## 20
## 0.11439047
## 21
## 0.11145738
## 22
## 0.07039413
## 23
## 0.01393217
## 25
## 0.01466544
##
## [[22]]
##
## Female
## 0.59816682
## Male
## 0.38790101
## Non-Binary
## 0.01393217
##
## [[23]]
##
## Left-handed
## 0.09899175
## Right-handed
## 0.90100825
##
## [[24]]
##
## Hearing
## 1
##
## [[25]]
##
## Match
## 0.4551787
## Mismatch
## 0.5448213
##
## [[26]]
##
## Emotion-Label
## 0.13473877
## Emotion-Laden
## 0.17286893
## Mixed E.EL
## 0.08982585
## Mixed E.NEU
## 0.05132906
## Mixed EL.E
## 0.10907424
## Mixed EL.NEU
## 0.12172319
## Mixed NEU.E
## 0.07662695
## Mixed NEU.EL
## 0.19890009
## Neutral
## 0.04491292
##
## [[27]]
##
## Congruent
## 0.3140238
## Incongruent
## 0.2823098
## Pair 1 Congruent
## 0.1987168
## Pair 2 Congruent
## 0.2049496
##
## [[28]]
##
## Congruent
## 0.1024748
## Incongruent
## 0.4678277
## Pair 1 Congruent
## 0.2117324
## Pair 2 Congruent
## 0.2179652
##
## [[29]]
##
## Negative.Congruent
## 0.20531622
## Negative.Incongruent
## 0.07057745
## Negative.Pair 1 Congruent
## 0.07681027
## Negative.Pair 2 Congruent
## 0.10907424
## Neutral.Congruent
## 0.01924840
## Neutral.Incongruent
## 0.13473877
## Neutral.Pair 1 Congruent
## 0.03849679
## Neutral.Pair 2 Congruent
## 0.01924840
## Positive.Congruent
## 0.08945921
## Positive.Incongruent
## 0.07699358
## Positive.Pair 1 Congruent
## 0.08340972
## Positive.Pair 2 Congruent
## 0.07662695
##
## [[30]]
##
## Emotion-Label.Congruent
## 0.03849679
## Emotion-Label.Incongruent
## 0.21136572
## Emotion-Label.Pair 1 Congruent
## 0.05132906
## Emotion-Label.Pair 2 Congruent
## 0.05756187
## Emotion-Laden.Congruent
## 0.04472961
## Emotion-Laden.Incongruent
## 0.12172319
## Emotion-Laden.Pair 1 Congruent
## 0.12190651
## Emotion-Laden.Pair 2 Congruent
## 0.14115490
## Neutral.Congruent
## 0.01924840
## Neutral.Incongruent
## 0.13473877
## Neutral.Pair 1 Congruent
## 0.03849679
## Neutral.Pair 2 Congruent
## 0.01924840
##
## [[31]]
##
## Primed.Match
## 0.2245646
## Primed.Mismatch
## 0.2757104
## Satiated.Match
## 0.2306141
## Satiated.Mismatch
## 0.2691109
##
## [[32]]
## vars
## Reaction.Time 1
## bdi.tot 2
## stai.y1.tot 3
## stai.y2.tot 4
## Age 5
## n
## Reaction.Time 5356
## bdi.tot 5455
## stai.y1.tot 5455
## stai.y2.tot 5455
## Age 5455
## mean
## Reaction.Time 1442.91
## bdi.tot 10.58
## stai.y1.tot 38.28
## stai.y2.tot 41.77
## Age 19.26
## sd
## Reaction.Time 471.22
## bdi.tot 8.39
## stai.y1.tot 9.04
## stai.y2.tot 10.18
## Age 1.52
## median
## Reaction.Time 1361.5
## bdi.tot 8.0
## stai.y1.tot 37.0
## stai.y2.tot 40.0
## Age 19.0
## trimmed
## Reaction.Time 1401.17
## bdi.tot 9.46
## stai.y1.tot 37.18
## stai.y2.tot 41.23
## Age 19.00
## mad
## Reaction.Time 457.38
## bdi.tot 7.41
## stai.y1.tot 5.93
## stai.y2.tot 10.38
## Age 1.48
## min
## Reaction.Time 539
## bdi.tot 0
## stai.y1.tot 24
## stai.y2.tot 22
## Age 18
## max
## Reaction.Time 2999
## bdi.tot 38
## stai.y1.tot 62
## stai.y2.tot 67
## Age 25
## range
## Reaction.Time 2460
## bdi.tot 38
## stai.y1.tot 38
## stai.y2.tot 45
## Age 7
## skew
## Reaction.Time 0.77
## bdi.tot 1.18
## stai.y1.tot 0.94
## stai.y2.tot 0.50
## Age 1.34
## kurtosis
## Reaction.Time 0.21
## bdi.tot 1.11
## stai.y1.tot 0.34
## stai.y2.tot -0.46
## Age 1.64
## se
## Reaction.Time 6.44
## bdi.tot 0.11
## stai.y1.tot 0.12
## stai.y2.tot 0.14
## Age 0.02desc.pipe(sl.recoded.ind)## [[1]]
##
## n_a
## 0.2493126
## n_b
## 0.2637947
## pilota
## 0.2507791
## pilotb
## 0.2361137
##
## [[2]]
##
## Match
## 0.4707627
## Mismatch
## 0.5292373
##
## [[3]]
##
## C
## 0.63220339
## E
## 0.35021186
## NR
## 0.01758475
##
## [[4]]
##
## Primed
## 0.500275
## Satiated
## 0.499725
##
## [[5]]
##
## Negative
## 0.3712191
## Neutral
## 0.3140238
## Positive
## 0.3147571
##
## [[6]]
##
## Negative
## 0.3448137
## Neutral
## 0.4032226
## Positive
## 0.2519637
##
## [[7]]
##
## Negative
## 0.4010070
## Neutral
## 0.3659617
## Positive
## 0.2330312
##
## [[8]]
##
## Emotion-Label
## 0.2929423
## Emotion-Laden
## 0.3930339
## Neutral
## 0.3140238
##
## [[9]]
##
## Emotion-Label
## 0.2317140
## Emotion-Laden
## 0.4012832
## Neutral
## 0.3670027
##
## [[10]]
##
## Emotion-Label
## 0.2703941
## Emotion-Laden
## 0.3965170
## Neutral
## 0.3330889
##
## [[11]]
##
## Non-Scoring
## 0.6736939
## Scoring
## 0.3263061
##
## [[12]]
##
## Non-Scoring
## 0.7587534
## Scoring
## 0.2412466
##
## [[13]]
##
## Non-Scoring
## 0.5871677
## Scoring
## 0.4128323
##
## [[14]]
##
## Non-Scoring
## 0.4295142
## Scoring
## 0.5704858
##
## [[15]]
##
## Non-Scoring
## 0.5593034
## Scoring
## 0.4406966
##
## [[16]]
##
## Non-Scoring
## 0.5013749
## Scoring
## 0.4986251
##
## [[17]]
##
## BDI & STAI
## 0.1268561
## BDI Only
## 0.1994500
## Non-Scoring
## 0.5593034
## STAI Only
## 0.1143905
##
## [[18]]
##
## BDI & STAI
## 0.24051329
## BDI Only
## 0.08579285
## Non-Scoring
## 0.50137489
## STAI Only
## 0.17231897
##
## [[19]]
##
## BDI & STAI
## 0.24051329
## BDI Only
## 0.08579285
## Non-Scoring
## 0.42951421
## STAI Only
## 0.24417965
##
## [[20]]
##
## BDI & STAI Y1 & STAI Y2
## 0.12685610
## BDI & STAI Y2
## 0.11365720
## BDI Only
## 0.08579285
## Non-Scoring
## 0.42951421
## STAI Y1 & STAI Y2
## 0.04252979
## STAI Y1 Only
## 0.07186068
## STAI Y2 Only
## 0.12978918
##
## [[21]]
##
## 18
## 0.43189734
## 19
## 0.24326306
## 20
## 0.11439047
## 21
## 0.11145738
## 22
## 0.07039413
## 23
## 0.01393217
## 25
## 0.01466544
##
## [[22]]
##
## Female
## 0.59816682
## Male
## 0.38790101
## Non-Binary
## 0.01393217
##
## [[23]]
##
## Left-handed
## 0.09899175
## Right-handed
## 0.90100825
##
## [[24]]
##
## Hearing
## 1
##
## [[25]]
##
## Match
## 0.4707627
## Mismatch
## 0.5292373
##
## [[26]]
##
## Emotion-Label
## 0.09770852
## Emotion-Laden
## 0.17855179
## Mixed E.EL
## 0.06746104
## Mixed E.NEU
## 0.06654445
## Mixed EL.E
## 0.09532539
## Mixed EL.NEU
## 0.12740605
## Mixed NEU.E
## 0.07736022
## Mixed NEU.EL
## 0.15050412
## Neutral
## 0.13913841
##
## [[27]]
##
## Congruent
## 0.2730932
## Incongruent
## 0.2747881
## Pair 1 Congruent
## 0.2273305
## Pair 2 Congruent
## 0.2247881
##
## [[28]]
##
## Congruent
## 0.1461045
## Incongruent
## 0.4573786
## Pair 1 Congruent
## 0.2082493
## Pair 2 Congruent
## 0.1882676
##
## [[29]]
##
## Negative.Congruent
## 0.10669111
## Negative.Incongruent
## 0.08230981
## Negative.NA
## 0.03006416
## Negative.Pair 1 Congruent
## 0.07112741
## Negative.Pair 2 Congruent
## 0.08102658
## Neutral.Congruent
## 0.05242896
## Neutral.Incongruent
## 0.07717690
## Neutral.NA
## 0.07424381
## Neutral.Pair 1 Congruent
## 0.06122823
## Neutral.Pair 2 Congruent
## 0.04894592
## Positive.Congruent
## 0.07717690
## Positive.Incongruent
## 0.07827681
## Positive.NA
## 0.03043080
## Positive.Pair 1 Congruent
## 0.06434464
## Positive.Pair 2 Congruent
## 0.06452796
##
## [[30]]
##
## Emotion-Label.Congruent
## 0.02584785
## Emotion-Label.Incongruent
## 0.18130156
## Emotion-Label.Pair 1 Congruent
## 0.03556370
## Emotion-Label.Pair 2 Congruent
## 0.05022915
## Emotion-Laden.Congruent
## 0.06782768
## Emotion-Laden.Incongruent
## 0.14555454
## Emotion-Laden.Pair 1 Congruent
## 0.09935839
## Emotion-Laden.Pair 2 Congruent
## 0.08029331
## Neutral.Congruent
## 0.05242896
## Neutral.Incongruent
## 0.13052246
## Neutral.Pair 1 Congruent
## 0.07332722
## Neutral.Pair 2 Congruent
## 0.05774519
##
## [[31]]
##
## Primed.Match
## 0.20238313
## Primed.Mismatch
## 0.22089826
## Primed.NA
## 0.07699358
## Satiated.Match
## 0.20494959
## Satiated.Mismatch
## 0.23703025
## Satiated.NA
## 0.05774519
##
## [[32]]
## vars
## Reaction.Time 1
## bdi.tot 2
## stai.y1.tot 3
## stai.y2.tot 4
## Age 5
## n
## Reaction.Time 5356
## bdi.tot 5455
## stai.y1.tot 5455
## stai.y2.tot 5455
## Age 5455
## mean
## Reaction.Time 1442.91
## bdi.tot 10.58
## stai.y1.tot 38.28
## stai.y2.tot 41.77
## Age 19.26
## sd
## Reaction.Time 471.22
## bdi.tot 8.39
## stai.y1.tot 9.04
## stai.y2.tot 10.18
## Age 1.52
## median
## Reaction.Time 1361.5
## bdi.tot 8.0
## stai.y1.tot 37.0
## stai.y2.tot 40.0
## Age 19.0
## trimmed
## Reaction.Time 1401.17
## bdi.tot 9.46
## stai.y1.tot 37.18
## stai.y2.tot 41.23
## Age 19.00
## mad
## Reaction.Time 457.38
## bdi.tot 7.41
## stai.y1.tot 5.93
## stai.y2.tot 10.38
## Age 1.48
## min
## Reaction.Time 539
## bdi.tot 0
## stai.y1.tot 24
## stai.y2.tot 22
## Age 18
## max
## Reaction.Time 2999
## bdi.tot 38
## stai.y1.tot 62
## stai.y2.tot 67
## Age 25
## range
## Reaction.Time 2460
## bdi.tot 38
## stai.y1.tot 38
## stai.y2.tot 45
## Age 7
## skew
## Reaction.Time 0.77
## bdi.tot 1.18
## stai.y1.tot 0.94
## stai.y2.tot 0.50
## Age 1.34
## kurtosis
## Reaction.Time 0.21
## bdi.tot 1.11
## stai.y1.tot 0.34
## stai.y2.tot -0.46
## Age 1.64
## se
## Reaction.Time 6.44
## bdi.tot 0.11
## stai.y1.tot 0.12
## stai.y2.tot 0.14
## Age 0.02desc.pipe(sl.removed.avg)## [[1]]
##
## n_a
## 0.2666872
## n_b
## 0.2823747
## pilota
## 0.2325438
## pilotb
## 0.2183943
##
## [[2]]
##
## Match
## 0.494617
## Mismatch
## 0.505383
##
## [[3]]
##
## C
## 0.82282375
## E
## 0.16210397
## NR
## 0.01507229
##
## [[4]]
##
## Primed
## 0.4733928
## Satiated
## 0.5266072
##
## [[5]]
##
## Negative
## 0.4629345
## Neutral
## 0.1722547
## Positive
## 0.3648108
##
## [[6]]
##
## Negative
## 0.3660412
## Neutral
## 0.2900646
## Positive
## 0.3438942
##
## [[7]]
##
## Negative
## 0.4952322
## Neutral
## 0.2153184
## Positive
## 0.2894494
##
## [[8]]
##
## Emotion-Label
## 0.4515534
## Emotion-Laden
## 0.3761919
## Neutral
## 0.1722547
##
## [[9]]
##
## Emotion-Label
## 0.3337435
## Emotion-Laden
## 0.3761919
## Neutral
## 0.2900646
##
## [[10]]
##
## Emotion-Label
## 0.3977238
## Emotion-Laden
## 0.3869579
## Neutral
## 0.2153184
##
## [[11]]
##
## Non-Scoring
## 0.6807136
## Scoring
## 0.3192864
##
## [[12]]
##
## Non-Scoring
## 0.762227
## Scoring
## 0.237773
##
## [[13]]
##
## Non-Scoring
## 0.5921255
## Scoring
## 0.4078745
##
## [[14]]
##
## Non-Scoring
## 0.4318671
## Scoring
## 0.5681329
##
## [[15]]
##
## Non-Scoring
## 0.5647493
## Scoring
## 0.4352507
##
## [[16]]
##
## Non-Scoring
## 0.5063058
## Scoring
## 0.4936942
##
## [[17]]
##
## BDI & STAI
## 0.1218087
## BDI Only
## 0.1974777
## Non-Scoring
## 0.5647493
## STAI Only
## 0.1159643
##
## [[18]]
##
## BDI & STAI
## 0.23346663
## BDI Only
## 0.08581975
## Non-Scoring
## 0.50630575
## STAI Only
## 0.17440787
##
## [[19]]
##
## BDI & STAI
## 0.23346663
## BDI Only
## 0.08581975
## Non-Scoring
## 0.43186712
## STAI Only
## 0.24884651
##
## [[20]]
##
## BDI & STAI Y1 & STAI Y2
## 0.12180867
## BDI & STAI Y2
## 0.11165795
## BDI Only
## 0.08581975
## Non-Scoring
## 0.43186712
## STAI Y1 & STAI Y2
## 0.04152568
## STAI Y1 Only
## 0.07443863
## STAI Y2 Only
## 0.13288219
##
## [[21]]
##
## 18
## 0.44017225
## 19
## 0.24454014
## 20
## 0.11442633
## 21
## 0.10489080
## 22
## 0.06736389
## 23
## 0.01291910
## 25
## 0.01568748
##
## [[22]]
##
## Female
## 0.5924331
## Male
## 0.3946478
## Non-Binary
## 0.0129191
##
## [[23]]
##
## Left-handed
## 0.09597047
## Right-handed
## 0.90402953
##
## [[24]]
##
## Hearing
## 1
##
## [[25]]
##
## Match
## 0.494617
## Mismatch
## 0.505383
##
## [[26]]
##
## Emotion-Label
## 0.20455245
## Emotion-Laden
## 0.18240541
## Mixed E.EL
## 0.06459551
## Mixed E.NEU
## 0.06459551
## Mixed EL.E
## 0.08612735
## Mixed EL.NEU
## 0.10765918
## Mixed NEU.E
## 0.10704399
## Mixed NEU.EL
## 0.13995694
## Neutral
## 0.04306367
##
## [[27]]
##
## Congruent
## 0.3869579
## Incongruent
## 0.1937865
## Pair 1 Congruent
## 0.1507229
## Pair 2 Congruent
## 0.2685328
##
## [[28]]
##
## Congruent
## 0.1073516
## Incongruent
## 0.5807444
## Pair 1 Congruent
## 0.1184251
## Pair 2 Congruent
## 0.1934789
##
## [[29]]
##
## Negative.Congruent
## 0.23685020
## Negative.Incongruent
## 0.03229775
## Negative.Pair 1 Congruent
## 0.06459551
## Negative.Pair 2 Congruent
## 0.12919102
## Neutral.Incongruent
## 0.15072285
## Neutral.Pair 2 Congruent
## 0.02153184
## Positive.Congruent
## 0.15010766
## Positive.Incongruent
## 0.01076592
## Positive.Pair 1 Congruent
## 0.08612735
## Positive.Pair 2 Congruent
## 0.11780990
##
## [[30]]
##
## Emotion-Label.Congruent
## 0.06459551
## Emotion-Label.Incongruent
## 0.24730852
## Emotion-Label.Pair 1 Congruent
## 0.05382959
## Emotion-Label.Pair 2 Congruent
## 0.08581975
## Emotion-Laden.Congruent
## 0.04275608
## Emotion-Laden.Incongruent
## 0.18271301
## Emotion-Laden.Pair 1 Congruent
## 0.06459551
## Emotion-Laden.Pair 2 Congruent
## 0.08612735
## Neutral.Incongruent
## 0.15072285
## Neutral.Pair 2 Congruent
## 0.02153184
##
## [[31]]
##
## Primed.Match
## 0.2045524
## Primed.Mismatch
## 0.2688404
## Satiated.Match
## 0.2900646
## Satiated.Mismatch
## 0.2365426
##
## [[32]]
## vars
## Reaction.Time 1
## bdi.tot 2
## stai.y1.tot 3
## stai.y2.tot 4
## Age 5
## n
## Reaction.Time 3202
## bdi.tot 3251
## stai.y1.tot 3251
## stai.y2.tot 3251
## Age 3251
## mean
## Reaction.Time 1388.55
## bdi.tot 10.40
## stai.y1.tot 38.22
## stai.y2.tot 41.65
## Age 19.23
## sd
## Reaction.Time 461.40
## bdi.tot 8.25
## stai.y1.tot 9.07
## stai.y2.tot 10.08
## Age 1.52
## median
## Reaction.Time 1300
## bdi.tot 8
## stai.y1.tot 37
## stai.y2.tot 40
## Age 19
## trimmed
## Reaction.Time 1341.71
## bdi.tot 9.30
## stai.y1.tot 37.10
## stai.y2.tot 41.12
## Age 18.97
## mad
## Reaction.Time 430.70
## bdi.tot 7.41
## stai.y1.tot 5.93
## stai.y2.tot 10.38
## Age 1.48
## min
## Reaction.Time 539
## bdi.tot 0
## stai.y1.tot 24
## stai.y2.tot 22
## Age 18
## max
## Reaction.Time 2999
## bdi.tot 38
## stai.y1.tot 62
## stai.y2.tot 67
## Age 25
## range
## Reaction.Time 2460
## bdi.tot 38
## stai.y1.tot 38
## stai.y2.tot 45
## Age 7
## skew
## Reaction.Time 0.90
## bdi.tot 1.20
## stai.y1.tot 0.96
## stai.y2.tot 0.50
## Age 1.42
## kurtosis
## Reaction.Time 0.53
## bdi.tot 1.22
## stai.y1.tot 0.36
## stai.y2.tot -0.43
## Age 1.92
## se
## Reaction.Time 8.15
## bdi.tot 0.14
## stai.y1.tot 0.16
## stai.y2.tot 0.18
## Age 0.03desc.pipe(sl.removed.ind)## [[1]]
##
## n_a
## 0.2552083
## n_b
## 0.2552083
## pilota
## 0.2460938
## pilotb
## 0.2434896
##
## [[2]]
##
## Match
## 0.7721354
## Mismatch
## 0.2278646
##
## [[3]]
##
## C
## 0.7434896
## E
## 0.2252604
## NR
## 0.0312500
##
## [[4]]
##
## Primed
## 0.4934896
## Satiated
## 0.5065104
##
## [[5]]
##
## Negative
## 0.3723958
## Neutral
## 0.4140625
## Positive
## 0.2135417
##
## [[6]]
##
## Negative
## 0.4713542
## Neutral
## 0.2682292
## Positive
## 0.2604167
##
## [[7]]
##
## Negative
## 0.4179688
## Neutral
## 0.2096354
## Positive
## 0.3723958
##
## [[8]]
##
## Emotion-Label
## 0.2382812
## Emotion-Laden
## 0.3476562
## Neutral
## 0.4140625
##
## [[9]]
##
## Emotion-Label
## 0.3619792
## Emotion-Laden
## 0.3697917
## Neutral
## 0.2682292
##
## [[10]]
##
## Emotion-Label
## 0.3658854
## Emotion-Laden
## 0.4244792
## Neutral
## 0.2096354
##
## [[11]]
##
## Non-Scoring
## 0.6367188
## Scoring
## 0.3632812
##
## [[12]]
##
## Non-Scoring
## 0.7408854
## Scoring
## 0.2591146
##
## [[13]]
##
## Non-Scoring
## 0.5572917
## Scoring
## 0.4427083
##
## [[14]]
##
## Non-Scoring
## 0.3841146
## Scoring
## 0.6158854
##
## [[15]]
##
## Non-Scoring
## 0.5117188
## Scoring
## 0.4882812
##
## [[16]]
##
## Non-Scoring
## 0.4544271
## Scoring
## 0.5455729
##
## [[17]]
##
## BDI & STAI
## 0.1341146
## BDI Only
## 0.2291667
## Non-Scoring
## 0.5117188
## STAI Only
## 0.1250000
##
## [[18]]
##
## BDI & STAI
## 0.2604167
## BDI Only
## 0.1028646
## Non-Scoring
## 0.4544271
## STAI Only
## 0.1822917
##
## [[19]]
##
## BDI & STAI
## 0.2604167
## BDI Only
## 0.1028646
## Non-Scoring
## 0.3841146
## STAI Only
## 0.2526042
##
## [[20]]
##
## BDI & STAI Y1 & STAI Y2
## 0.1341146
## BDI & STAI Y2
## 0.1263021
## BDI Only
## 0.1028646
## Non-Scoring
## 0.3841146
## STAI Y1 & STAI Y2
## 0.0546875
## STAI Y1 Only
## 0.0703125
## STAI Y2 Only
## 0.1276042
##
## [[21]]
##
## 18
## 0.44401042
## 19
## 0.22916667
## 20
## 0.10026042
## 21
## 0.10937500
## 22
## 0.07942708
## 23
## 0.01953125
## 25
## 0.01822917
##
## [[22]]
##
## Female
## 0.5898438
## Male
## 0.3945312
## Non-Binary
## 0.0156250
##
## [[23]]
##
## Left-handed
## 0.1132812
## Right-handed
## 0.8867188
##
## [[24]]
##
## Hearing
## 1
##
## [[25]]
##
## Match
## 0.7721354
## Mismatch
## 0.2278646
##
## [[26]]
##
## Emotion-Label
## 0.250000000
## Emotion-Laden
## 0.186197917
## Mixed E.EL
## 0.100260417
## Mixed E.NEU
## 0.011718750
## Mixed EL.E
## 0.110677083
## Mixed EL.NEU
## 0.072916667
## Mixed NEU.E
## 0.005208333
## Mixed NEU.EL
## 0.138020833
## Neutral
## 0.125000000
##
## [[27]]
##
## Congruent
## 0.60937500
## Incongruent
## 0.16276042
## Pair 1 Congruent
## 0.16406250
## Pair 2 Congruent
## 0.06380208
##
## [[28]]
##
## Congruent
## 0.17447917
## Incongruent
## 0.51302083
## Pair 1 Congruent
## 0.23307292
## Pair 2 Congruent
## 0.07942708
##
## [[29]]
##
## Negative.Congruent
## 0.369791667
## Negative.Incongruent
## 0.001302083
## Negative.Pair 1 Congruent
## 0.001302083
## Neutral.Congruent
## 0.096354167
## Neutral.Incongruent
## 0.134114583
## Neutral.Pair 1 Congruent
## 0.131510417
## Neutral.Pair 2 Congruent
## 0.052083333
## Positive.Congruent
## 0.143229167
## Positive.Incongruent
## 0.027343750
## Positive.Pair 1 Congruent
## 0.031250000
## Positive.Pair 2 Congruent
## 0.011718750
##
## [[30]]
##
## Emotion-Label.Congruent
## 0.03645833
## Emotion-Label.Incongruent
## 0.16406250
## Emotion-Label.Pair 1 Congruent
## 0.02343750
## Emotion-Label.Pair 2 Congruent
## 0.01432292
## Emotion-Laden.Congruent
## 0.04166667
## Emotion-Laden.Incongruent
## 0.21484375
## Emotion-Laden.Pair 1 Congruent
## 0.07812500
## Emotion-Laden.Pair 2 Congruent
## 0.01302083
## Neutral.Congruent
## 0.09635417
## Neutral.Incongruent
## 0.13411458
## Neutral.Pair 1 Congruent
## 0.13151042
## Neutral.Pair 2 Congruent
## 0.05208333
##
## [[31]]
##
## Primed.Match
## 0.42187500
## Primed.Mismatch
## 0.07161458
## Satiated.Match
## 0.35026042
## Satiated.Mismatch
## 0.15625000
##
## [[32]]
## vars
## Reaction.Time 1
## bdi.tot 2
## stai.y1.tot 3
## stai.y2.tot 4
## Age 5
## n
## Reaction.Time 744
## bdi.tot 768
## stai.y1.tot 768
## stai.y2.tot 768
## Age 768
## mean
## Reaction.Time 1442.59
## bdi.tot 11.24
## stai.y1.tot 38.67
## stai.y2.tot 42.32
## Age 19.30
## sd
## Reaction.Time 485.27
## bdi.tot 8.68
## stai.y1.tot 8.91
## stai.y2.tot 10.50
## Age 1.61
## median
## Reaction.Time 1362
## bdi.tot 8
## stai.y1.tot 37
## stai.y2.tot 40
## Age 19
## trimmed
## Reaction.Time 1404.69
## bdi.tot 10.25
## stai.y1.tot 37.70
## stai.y2.tot 41.76
## Age 19.04
## mad
## Reaction.Time 500.38
## bdi.tot 8.90
## stai.y1.tot 5.93
## stai.y2.tot 10.38
## Age 1.48
## min
## Reaction.Time 574
## bdi.tot 0
## stai.y1.tot 24
## stai.y2.tot 22
## Age 18
## max
## Reaction.Time 2945
## bdi.tot 38
## stai.y1.tot 62
## stai.y2.tot 67
## Age 25
## range
## Reaction.Time 2371
## bdi.tot 38
## stai.y1.tot 38
## stai.y2.tot 45
## Age 7
## skew
## Reaction.Time 0.63
## bdi.tot 1.02
## stai.y1.tot 0.87
## stai.y2.tot 0.48
## Age 1.33
## kurtosis
## Reaction.Time -0.19
## bdi.tot 0.56
## stai.y1.tot 0.19
## stai.y2.tot -0.58
## Age 1.41
## se
## Reaction.Time 17.79
## bdi.tot 0.31
## stai.y1.tot 0.32
## stai.y2.tot 0.38
## Age 0.0613 Accuracy Analyses
rt.participant <- aggregate(sl.trimmed$Reaction.Time, by = list(sl.trimmed$Session.ID), FUN = mean, na.rm = TRUE) %>%
rename(., Session.ID = "Group.1", Reaction.Time = "x")
# Accuracy By Participant Pipes
accuracy.df.pipe <- function(sldf) {
sldf.accuracy <- sldf %>%
select(., c(Session.ID, Error.Code)) %>%
table(.) %>%
data.frame(.) %>%
recast(., formula = Session.ID ~ Error.Code, id.var = c("Session.ID", "Error.Code")) %>%
merge(., rt.participant, by = "Session.ID") %>%
mutate(.,
Response.Rate = (.$C + .$E) / (.$C + .$E + .$NR),
Accuracy = .$C / (.$C + .$E)
) %>%
return(.)
}
accuracy.vis.pipe <- function(sldf.accuracy) {
return(
list(
formattable(describe(sldf.accuracy)),
plot_histogram(sldf.accuracy$Accuracy),
plot_histogram(sldf.accuracy$Response.Rate),
plot_histogram(sldf.accuracy$Reaction.Time),
boxplot(sldf.accuracy$Response.Rate),
boxplot(sldf.accuracy$Accuracy),
boxplot(sldf.accuracy$Reaction.Time),
formattable(cor(sldf.accuracy[2:7]))
)
)
}
accuracy.trial.pipe <- function(sldf) {
acc.trial <- sldf %>%
recast(., formula = Trial.ID ~ Error.Code, id.var = c("Trial.ID", "Error.Code")) %>%
mutate(.,
Response.Rate = (.$C + .$E) / (.$C + .$E + .$NR),
Accuracy = .$C / (.$C + .$E)
)
return(
list(
formattable(acc.trial),
plot_histogram(acc.trial$Accuracy)
)
)
}
# Apply Pipes to Data Frames
acc.sl.trimmed <- sl.trimmed %>%
accuracy.df.pipe(.) %T>%
accuracy.vis.pipe(.)
acc.sl.recoded.avg <- sl.recoded.avg %>%
accuracy.df.pipe(.) %T>%
accuracy.vis.pipe(.)
acc.sl.recoded.ind <- sl.recoded.ind %>%
accuracy.df.pipe(.) %T>%
accuracy.vis.pipe(.)
acc.sl.removed.avg <- sl.removed.avg %>%
accuracy.df.pipe(.) %T>%
accuracy.vis.pipe(.)
acc.sl.removed.ind <- sl.removed.ind %>%
accuracy.df.pipe(.) %T>%
accuracy.vis.pipe(.)
accuracy.trial.pipe(sl.trimmed)
## [[1]]
##
## [[2]]
## [[2]]$page_1
accuracy.trial.pipe(sl.recoded.avg)
## [[1]]
##
## [[2]]
## [[2]]$page_1
accuracy.trial.pipe(sl.recoded.ind)
## [[1]]
##
## [[2]]
## [[2]]$page_1
accuracy.trial.pipe(sl.removed.avg)
## [[1]]
##
## [[2]]
## [[2]]$page_1
accuracy.trial.pipe(sl.removed.ind)
## [[1]]
##
## [[2]]
## [[2]]$page_1
14 ANOVAs
#aov.pipe for the main anova analyses
aov.pipe <- function(sldf){
return(list(planned3=jmv::ANOVA(
formula = Reaction.Time ~ X.Primed.Satiated * Word.Type.n * Valence.Congruency.n,
data = dplyr::filter(sldf, bdi == "Non-Scoring" & Error.Code == "C"),
effectSize = c("eta", "partEta"),
homo = TRUE,
norm = TRUE,
qq = TRUE,
postHoc = ~ X.Primed.Satiated * Word.Type.n * Valence.Congruency.n,
postHocCorr = c("tukey", "scheffe"),
emMeans = ~ X.Primed.Satiated * Word.Type.n * Valence.Congruency.n,
emmTables = TRUE),
wt3=jmv::ANOVA(
formula = Reaction.Time ~ bdi * X.Primed.Satiated * Target.WT,
data = dplyr::filter(sldf, Error.Code == "C"),
effectSize = c("eta", "partEta"),
homo = TRUE,
norm = TRUE,
qq = TRUE,
postHoc = ~ bdi * X.Primed.Satiated * Target.WT,
postHocCorr = c("tukey", "scheffe"),
emMeans = ~ bdi * X.Primed.Satiated * Target.WT,
emmTables = TRUE),
v3=jmv::ANOVA(
formula = Reaction.Time ~ bdi * X.Primed.Satiated * Valence.Congruency.n,
data = dplyr::filter(sldf, Error.Code == "C"),
effectSize = c("eta", "partEta"),
homo = TRUE,
norm = TRUE,
qq = TRUE,
postHoc = ~ bdi * X.Primed.Satiated * Valence.Congruency.n,
postHocCorr = c("tukey", "scheffe"),
emMeans = ~ bdi * X.Primed.Satiated * Valence.Congruency.n,
emmTables = TRUE),
rta4=jmv::ANOVA(
formula = Reaction.Time ~ bdi * X.Primed.Satiated * Word.Type.n * Valence.Congruency.n,
data = sldf,
effectSize = c("eta", "partEta"),
homo = TRUE,
norm = TRUE,
qq = TRUE,
postHoc = ~ bdi * X.Primed.Satiated * Word.Type.n * Valence.Congruency.n,
postHocCorr = c("tukey", "scheffe"),
emMeans = ~ bdi * X.Primed.Satiated * Word.Type.n * Valence.Congruency.n,
emmTables = TRUE),
rtc4=jmv::ANOVA(
formula = Reaction.Time ~ bdi * X.Primed.Satiated * Word.Type.n * Valence.Congruency.n,
data = dplyr::filter(sldf, Error.Code == "C"),
effectSize = c("eta", "partEta"),
homo = TRUE,
norm = TRUE,
qq = TRUE,
postHoc = ~ bdi * X.Primed.Satiated * Word.Type.n * Valence.Congruency.n,
postHocCorr = c("tukey", "scheffe"),
emMeans = ~ bdi * X.Primed.Satiated * Word.Type.n * Valence.Congruency.n,
emmTables = TRUE)))
}
# aov.pipe(sl.trimmed)
#aov.pipe(sl.recoded.avg)
# aov.pipe(sl.recoded.ind) # singular
# aov.pipe(sl.removed.avg) # Too many missing values, imbalanced, disregard
# aov.pipe(sl.removed.ind) # Too many missing values, imbalanced, disregard15 Linear Models
sl.regression <- sl.trimmed %>%
merge(., sam.cat.comp.val[c("Target", "Rated.Mean", "ANEW.Mean", "Difference", "Rated.Category", "ANEW.Category")], by = "Target") %>%
merge(., sam.comp.ar[c("Target", "Rated.Mean", "ANEW.Mean", "Difference")], by = "Target") %>%
rename(.,
"Valence.Rated.Mean" = Rated.Mean.x,
"Valence.ANEW.Mean" = ANEW.Mean.x,
"Valence.Difference" = Difference.x,
"Arousal.Rated.Mean" = Rated.Mean.y,
"Arousal.ANEW.Mean" = ANEW.Mean.y,
"Arousal.Difference" = Difference.y
) %>%
mutate(.,
Valence.ANEW.abs = abs(.$Valence.ANEW.Mean - 4.5),
Valence.Rated.abs = abs(.$Valence.Rated.Mean - 4.5),
Arousal.ANEW.abs = abs(.$Arousal.ANEW.Mean - 4.5),
Arousal.Rated.abs = abs(.$Arousal.Rated.Mean - 4.5)
)
#write.csv(sl.regression, file = "sl.regression.csv")
big.glm<-glm(Reaction.Time ~
Valence.ANEW.Mean +
Arousal.ANEW.Mean +
Valence.Rated.Mean +
Arousal.Rated.Mean +
Valence.ANEW.abs +
Arousal.ANEW.abs +
Valence.Rated.abs +
Arousal.Rated.abs +
bdi.tot +
stai.y1.tot +
stai.y2.tot +
X.Primed.Satiated +
Target.WT,
data = sl.regression
)
baby.glm<-glm(Reaction.Time ~
Valence.ANEW.Mean +
bdi.tot +
stai.y1.tot +
X.Primed.Satiated +
Target.WT,
data = sl.regression
)
big.glm##
## Call: glm(formula = Reaction.Time ~ Valence.ANEW.Mean + Arousal.ANEW.Mean +
## Valence.Rated.Mean + Arousal.Rated.Mean + Valence.ANEW.abs +
## Arousal.ANEW.abs + Valence.Rated.abs + Arousal.Rated.abs +
## bdi.tot + stai.y1.tot + stai.y2.tot + X.Primed.Satiated +
## Target.WT, data = sl.regression)
##
## Coefficients:
## (Intercept)
## 939.569
## Valence.ANEW.Mean
## 19.582
## Arousal.ANEW.Mean
## 41.213
## Valence.Rated.Mean
## -29.960
## Arousal.Rated.Mean
## 15.824
## Valence.ANEW.abs
## 42.039
## Arousal.ANEW.abs
## -76.882
## Valence.Rated.abs
## -29.946
## Arousal.Rated.abs
## -5.260
## bdi.tot
## 3.081
## stai.y1.tot
## 3.573
## stai.y2.tot
## 1.675
## X.Primed.SatiatedSatiated
## 19.780
## Target.WTEmotion-Laden
## -4.127
## Target.WTNeutral
## 238.829
##
## Degrees of Freedom: 5355 Total (i.e. Null); 5341 Residual
## (99 observations deleted due to missingness)
## Null Deviance: 1.189e+09
## Residual Deviance: 1.112e+09 AIC: 80810summary(big.glm)##
## Call:
## glm(formula = Reaction.Time ~ Valence.ANEW.Mean + Arousal.ANEW.Mean +
## Valence.Rated.Mean + Arousal.Rated.Mean + Valence.ANEW.abs +
## Arousal.ANEW.abs + Valence.Rated.abs + Arousal.Rated.abs +
## bdi.tot + stai.y1.tot + stai.y2.tot + X.Primed.Satiated +
## Target.WT, data = sl.regression)
##
## Deviance Residuals:
## Min
## -1014.0
## 1Q
## -337.1
## Median
## -87.9
## 3Q
## 267.6
## Max
## 1607.8
##
## Coefficients:
## Estimate
## (Intercept) 939.5691
## Valence.ANEW.Mean 19.5816
## Arousal.ANEW.Mean 41.2126
## Valence.Rated.Mean -29.9604
## Arousal.Rated.Mean 15.8244
## Valence.ANEW.abs 42.0389
## Arousal.ANEW.abs -76.8815
## Valence.Rated.abs -29.9461
## Arousal.Rated.abs -5.2604
## bdi.tot 3.0806
## stai.y1.tot 3.5726
## stai.y2.tot 1.6750
## X.Primed.SatiatedSatiated 19.7797
## Target.WTEmotion-Laden -4.1269
## Target.WTNeutral 238.8287
## Std. Error
## (Intercept) 116.8952
## Valence.ANEW.Mean 7.0821
## Arousal.ANEW.Mean 26.0015
## Valence.Rated.Mean 7.1823
## Arousal.Rated.Mean 11.6970
## Valence.ANEW.abs 17.5215
## Arousal.ANEW.abs 27.6147
## Valence.Rated.abs 11.3341
## Arousal.Rated.abs 13.6025
## bdi.tot 1.0917
## stai.y1.tot 0.8185
## stai.y2.tot 0.9018
## X.Primed.SatiatedSatiated 12.5953
## Target.WTEmotion-Laden 15.1841
## Target.WTNeutral 43.3184
## t value
## (Intercept) 8.038
## Valence.ANEW.Mean 2.765
## Arousal.ANEW.Mean 1.585
## Valence.Rated.Mean -4.171
## Arousal.Rated.Mean 1.353
## Valence.ANEW.abs 2.399
## Arousal.ANEW.abs -2.784
## Valence.Rated.abs -2.642
## Arousal.Rated.abs -0.387
## bdi.tot 2.822
## stai.y1.tot 4.365
## stai.y2.tot 1.857
## X.Primed.SatiatedSatiated 1.570
## Target.WTEmotion-Laden -0.272
## Target.WTNeutral 5.513
## Pr(>|t|)
## (Intercept) 1.12e-15
## Valence.ANEW.Mean 0.00571
## Arousal.ANEW.Mean 0.11302
## Valence.Rated.Mean 3.08e-05
## Arousal.Rated.Mean 0.17616
## Valence.ANEW.abs 0.01646
## Arousal.ANEW.abs 0.00539
## Valence.Rated.abs 0.00826
## Arousal.Rated.abs 0.69898
## bdi.tot 0.00479
## stai.y1.tot 1.30e-05
## stai.y2.tot 0.06331
## X.Primed.SatiatedSatiated 0.11638
## Target.WTEmotion-Laden 0.78579
## Target.WTNeutral 3.69e-08
##
## (Intercept) ***
## Valence.ANEW.Mean **
## Arousal.ANEW.Mean
## Valence.Rated.Mean ***
## Arousal.Rated.Mean
## Valence.ANEW.abs *
## Arousal.ANEW.abs **
## Valence.Rated.abs **
## Arousal.Rated.abs
## bdi.tot **
## stai.y1.tot ***
## stai.y2.tot .
## X.Primed.SatiatedSatiated
## Target.WTEmotion-Laden
## Target.WTNeutral ***
## ---
## Signif. codes:
## 0 '***'
## 0.001 '**'
## 0.01 '*'
## 0.05 '.'
## 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 208207.5)
##
## Null deviance: 1189058537 on 5355 degrees of freedom
## Residual deviance: 1112036168 on 5341 degrees of freedom
## (99 observations deleted due to missingness)
## AIC: 80808
##
## Number of Fisher Scoring iterations: 2summ(big.glm)| Observations | 5356 (99 missing obs. deleted) |
| Dependent variable | Reaction.Time |
| Type | Linear regression |
| 𝛘²(14) | 77022369.58 |
| Pseudo-R² (Cragg-Uhler) | 0.06 |
| Pseudo-R² (McFadden) | 0.00 |
| AIC | 80807.78 |
| BIC | 80913.16 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 939.57 | 116.90 | 8.04 | 0.00 |
| Valence.ANEW.Mean | 19.58 | 7.08 | 2.76 | 0.01 |
| Arousal.ANEW.Mean | 41.21 | 26.00 | 1.59 | 0.11 |
| Valence.Rated.Mean | -29.96 | 7.18 | -4.17 | 0.00 |
| Arousal.Rated.Mean | 15.82 | 11.70 | 1.35 | 0.18 |
| Valence.ANEW.abs | 42.04 | 17.52 | 2.40 | 0.02 |
| Arousal.ANEW.abs | -76.88 | 27.61 | -2.78 | 0.01 |
| Valence.Rated.abs | -29.95 | 11.33 | -2.64 | 0.01 |
| Arousal.Rated.abs | -5.26 | 13.60 | -0.39 | 0.70 |
| bdi.tot | 3.08 | 1.09 | 2.82 | 0.00 |
| stai.y1.tot | 3.57 | 0.82 | 4.36 | 0.00 |
| stai.y2.tot | 1.68 | 0.90 | 1.86 | 0.06 |
| X.Primed.SatiatedSatiated | 19.78 | 12.60 | 1.57 | 0.12 |
| Target.WTEmotion-Laden | -4.13 | 15.18 | -0.27 | 0.79 |
| Target.WTNeutral | 238.83 | 43.32 | 5.51 | 0.00 |
| Standard errors: MLE |
stepAIC(big.glm)## Start: AIC=80807.78
## Reaction.Time ~ Valence.ANEW.Mean + Arousal.ANEW.Mean + Valence.Rated.Mean +
## Arousal.Rated.Mean + Valence.ANEW.abs + Arousal.ANEW.abs +
## Valence.Rated.abs + Arousal.Rated.abs + bdi.tot + stai.y1.tot +
## stai.y2.tot + X.Primed.Satiated + Target.WT
##
## Df
## - Arousal.Rated.abs 1
## - Arousal.Rated.Mean 1
## <none>
## - X.Primed.Satiated 1
## - Arousal.ANEW.Mean 1
## - stai.y2.tot 1
## - Valence.ANEW.abs 1
## - Valence.Rated.abs 1
## - Valence.ANEW.Mean 1
## - Arousal.ANEW.abs 1
## - bdi.tot 1
## - Valence.Rated.Mean 1
## - stai.y1.tot 1
## - Target.WT 2
## Deviance
## - Arousal.Rated.abs 1112067306
## - Arousal.Rated.Mean 1112417238
## <none> 1112036168
## - X.Primed.Satiated 1112549638
## - Arousal.ANEW.Mean 1112559239
## - stai.y2.tot 1112754475
## - Valence.ANEW.abs 1113234721
## - Valence.Rated.abs 1113489637
## - Valence.ANEW.Mean 1113627906
## - Arousal.ANEW.abs 1113650000
## - bdi.tot 1113694158
## - Valence.Rated.Mean 1115659127
## - stai.y1.tot 1116002457
## - Target.WT 1118684352
## AIC
## - Arousal.Rated.abs 80806
## - Arousal.Rated.Mean 80808
## <none> 80808
## - X.Primed.Satiated 80808
## - Arousal.ANEW.Mean 80808
## - stai.y2.tot 80809
## - Valence.ANEW.abs 80812
## - Valence.Rated.abs 80813
## - Valence.ANEW.Mean 80813
## - Arousal.ANEW.abs 80814
## - bdi.tot 80814
## - Valence.Rated.Mean 80823
## - stai.y1.tot 80825
## - Target.WT 80836
##
## Step: AIC=80805.93
## Reaction.Time ~ Valence.ANEW.Mean + Arousal.ANEW.Mean + Valence.Rated.Mean +
## Arousal.Rated.Mean + Valence.ANEW.abs + Arousal.ANEW.abs +
## Valence.Rated.abs + bdi.tot + stai.y1.tot + stai.y2.tot +
## X.Primed.Satiated + Target.WT
##
## Df
## - Arousal.Rated.Mean 1
## <none>
## - X.Primed.Satiated 1
## - Arousal.ANEW.Mean 1
## - stai.y2.tot 1
## - Valence.ANEW.abs 1
## - Valence.Rated.abs 1
## - bdi.tot 1
## - Valence.ANEW.Mean 1
## - Arousal.ANEW.abs 1
## - stai.y1.tot 1
## - Valence.Rated.Mean 1
## - Target.WT 2
## Deviance
## - Arousal.Rated.Mean 1112468932
## <none> 1112067306
## - X.Primed.Satiated 1112583463
## - Arousal.ANEW.Mean 1112722828
## - stai.y2.tot 1112785246
## - Valence.ANEW.abs 1113260550
## - Valence.Rated.abs 1113522993
## - bdi.tot 1113727543
## - Valence.ANEW.Mean 1113887516
## - Arousal.ANEW.abs 1114007469
## - stai.y1.tot 1116032788
## - Valence.Rated.Mean 1116086809
## - Target.WT 1118715489
## AIC
## - Arousal.Rated.Mean 80806
## <none> 80806
## - X.Primed.Satiated 80806
## - Arousal.ANEW.Mean 80807
## - stai.y2.tot 80807
## - Valence.ANEW.abs 80810
## - Valence.Rated.abs 80811
## - bdi.tot 80812
## - Valence.ANEW.Mean 80813
## - Arousal.ANEW.abs 80813
## - stai.y1.tot 80823
## - Valence.Rated.Mean 80823
## - Target.WT 80834
##
## Step: AIC=80805.86
## Reaction.Time ~ Valence.ANEW.Mean + Arousal.ANEW.Mean + Valence.Rated.Mean +
## Valence.ANEW.abs + Arousal.ANEW.abs + Valence.Rated.abs +
## bdi.tot + stai.y1.tot + stai.y2.tot + X.Primed.Satiated +
## Target.WT
##
## Df
## <none>
## - X.Primed.Satiated 1
## - stai.y2.tot 1
## - Valence.Rated.abs 1
## - Valence.ANEW.abs 1
## - Arousal.ANEW.Mean 1
## - bdi.tot 1
## - Valence.ANEW.Mean 1
## - Arousal.ANEW.abs 1
## - Valence.Rated.Mean 1
## - stai.y1.tot 1
## - Target.WT 2
## Deviance
## <none> 1112468932
## - X.Primed.Satiated 1112954348
## - stai.y2.tot 1113188489
## - Valence.Rated.abs 1113525372
## - Valence.ANEW.abs 1113574836
## - Arousal.ANEW.Mean 1113704749
## - bdi.tot 1114123158
## - Valence.ANEW.Mean 1114277007
## - Arousal.ANEW.abs 1114513167
## - Valence.Rated.Mean 1116414738
## - stai.y1.tot 1116436431
## - Target.WT 1119551200
## AIC
## <none> 80806
## - X.Primed.Satiated 80806
## - stai.y2.tot 80807
## - Valence.Rated.abs 80809
## - Valence.ANEW.abs 80809
## - Arousal.ANEW.Mean 80810
## - bdi.tot 80812
## - Valence.ANEW.Mean 80813
## - Arousal.ANEW.abs 80814
## - Valence.Rated.Mean 80823
## - stai.y1.tot 80823
## - Target.WT 80836##
## Call: glm(formula = Reaction.Time ~ Valence.ANEW.Mean + Arousal.ANEW.Mean +
## Valence.Rated.Mean + Valence.ANEW.abs + Arousal.ANEW.abs +
## Valence.Rated.abs + bdi.tot + stai.y1.tot + stai.y2.tot +
## X.Primed.Satiated + Target.WT, data = sl.regression)
##
## Coefficients:
## (Intercept)
## 914.2974
## Valence.ANEW.Mean
## 20.2010
## Arousal.ANEW.Mean
## 56.5557
## Valence.Rated.Mean
## -30.3329
## Valence.ANEW.abs
## 40.2834
## Arousal.ANEW.abs
## -82.1774
## Valence.Rated.abs
## -21.1724
## bdi.tot
## 3.0771
## stai.y1.tot
## 3.5731
## stai.y2.tot
## 1.6765
## X.Primed.SatiatedSatiated
## 19.2189
## Target.WTEmotion-Laden
## 0.4267
## Target.WTNeutral
## 247.5221
##
## Degrees of Freedom: 5355 Total (i.e. Null); 5343 Residual
## (99 observations deleted due to missingness)
## Null Deviance: 1.189e+09
## Residual Deviance: 1.112e+09 AIC: 80810baby.glm##
## Call: glm(formula = Reaction.Time ~ Valence.ANEW.Mean + bdi.tot + stai.y1.tot +
## X.Primed.Satiated + Target.WT, data = sl.regression)
##
## Coefficients:
## (Intercept)
## 1179.5130
## Valence.ANEW.Mean
## -0.9706
## bdi.tot
## 4.2605
## stai.y1.tot
## 4.0200
## X.Primed.SatiatedSatiated
## 16.5076
## Target.WTEmotion-Laden
## 13.1824
## Target.WTNeutral
## 220.3413
##
## Degrees of Freedom: 5355 Total (i.e. Null); 5349 Residual
## (99 observations deleted due to missingness)
## Null Deviance: 1.189e+09
## Residual Deviance: 1.121e+09 AIC: 80840summary(baby.glm)##
## Call:
## glm(formula = Reaction.Time ~ Valence.ANEW.Mean + bdi.tot + stai.y1.tot +
## X.Primed.Satiated + Target.WT, data = sl.regression)
##
## Deviance Residuals:
## Min
## -953.32
## 1Q
## -341.70
## Median
## -88.84
## 3Q
## 263.57
## Max
## 1622.70
##
## Coefficients:
## Estimate
## (Intercept) 1179.5130
## Valence.ANEW.Mean -0.9706
## bdi.tot 4.2605
## stai.y1.tot 4.0200
## X.Primed.SatiatedSatiated 16.5076
## Target.WTEmotion-Laden 13.1824
## Target.WTNeutral 220.3413
## Std. Error
## (Intercept) 32.1603
## Valence.ANEW.Mean 2.7117
## bdi.tot 0.8775
## stai.y1.tot 0.7857
## X.Primed.SatiatedSatiated 12.5130
## Target.WTEmotion-Laden 14.5067
## Target.WTNeutral 16.0899
## t value
## (Intercept) 36.676
## Valence.ANEW.Mean -0.358
## bdi.tot 4.855
## stai.y1.tot 5.116
## X.Primed.SatiatedSatiated 1.319
## Target.WTEmotion-Laden 0.909
## Target.WTNeutral 13.694
## Pr(>|t|)
## (Intercept) < 2e-16
## Valence.ANEW.Mean 0.720
## bdi.tot 1.24e-06
## stai.y1.tot 3.23e-07
## X.Primed.SatiatedSatiated 0.187
## Target.WTEmotion-Laden 0.364
## Target.WTNeutral < 2e-16
##
## (Intercept) ***
## Valence.ANEW.Mean
## bdi.tot ***
## stai.y1.tot ***
## X.Primed.SatiatedSatiated
## Target.WTEmotion-Laden
## Target.WTNeutral ***
## ---
## Signif. codes:
## 0 '***'
## 0.001 '**'
## 0.01 '*'
## 0.05 '.'
## 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 209642.2)
##
## Null deviance: 1189058537 on 5355 degrees of freedom
## Residual deviance: 1121376326 on 5349 degrees of freedom
## (99 observations deleted due to missingness)
## AIC: 80837
##
## Number of Fisher Scoring iterations: 2summ(baby.glm)| Observations | 5356 (99 missing obs. deleted) |
| Dependent variable | Reaction.Time |
| Type | Linear regression |
| 𝛘²(6) | 67682211.69 |
| Pseudo-R² (Cragg-Uhler) | 0.06 |
| Pseudo-R² (McFadden) | 0.00 |
| AIC | 80836.58 |
| BIC | 80889.27 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 1179.51 | 32.16 | 36.68 | 0.00 |
| Valence.ANEW.Mean | -0.97 | 2.71 | -0.36 | 0.72 |
| bdi.tot | 4.26 | 0.88 | 4.86 | 0.00 |
| stai.y1.tot | 4.02 | 0.79 | 5.12 | 0.00 |
| X.Primed.SatiatedSatiated | 16.51 | 12.51 | 1.32 | 0.19 |
| Target.WTEmotion-Laden | 13.18 | 14.51 | 0.91 | 0.36 |
| Target.WTNeutral | 220.34 | 16.09 | 13.69 | 0.00 |
| Standard errors: MLE |
stepAIC(baby.glm)## Start: AIC=80836.58
## Reaction.Time ~ Valence.ANEW.Mean + bdi.tot + stai.y1.tot + X.Primed.Satiated +
## Target.WT
##
## Df
## - Valence.ANEW.Mean 1
## - X.Primed.Satiated 1
## <none>
## - bdi.tot 1
## - stai.y1.tot 1
## - Target.WT 2
## Deviance
## - Valence.ANEW.Mean 1121403184
## - X.Primed.Satiated 1121741188
## <none> 1121376326
## - bdi.tot 1126318536
## - stai.y1.tot 1126863767
## - Target.WT 1167979783
## AIC
## - Valence.ANEW.Mean 80835
## - X.Primed.Satiated 80836
## <none> 80837
## - bdi.tot 80858
## - stai.y1.tot 80861
## - Target.WT 81051
##
## Step: AIC=80834.71
## Reaction.Time ~ bdi.tot + stai.y1.tot + X.Primed.Satiated + Target.WT
##
## Df
## - X.Primed.Satiated 1
## <none>
## - bdi.tot 1
## - stai.y1.tot 1
## - Target.WT 2
## Deviance
## - X.Primed.Satiated 1121768986
## <none> 1121403184
## - bdi.tot 1126520427
## - stai.y1.tot 1126986727
## - Target.WT 1168099025
## AIC
## - X.Primed.Satiated 80834
## <none> 80835
## - bdi.tot 80857
## - stai.y1.tot 80859
## - Target.WT 81049
##
## Step: AIC=80834.45
## Reaction.Time ~ bdi.tot + stai.y1.tot + Target.WT
##
## Df
## <none>
## - bdi.tot 1
## - stai.y1.tot 1
## - Target.WT 2
## Deviance
## <none> 1121768986
## - bdi.tot 1126879530
## - stai.y1.tot 1127354756
## - Target.WT 1168450724
## AIC
## <none> 80834
## - bdi.tot 80857
## - stai.y1.tot 80859
## - Target.WT 81049##
## Call: glm(formula = Reaction.Time ~ bdi.tot + stai.y1.tot + Target.WT,
## data = sl.regression)
##
## Coefficients:
## (Intercept)
## 1183.131
## bdi.tot
## 4.173
## stai.y1.tot
## 4.043
## Target.WTEmotion-Laden
## 13.034
## Target.WTNeutral
## 220.380
##
## Degrees of Freedom: 5355 Total (i.e. Null); 5351 Residual
## (99 observations deleted due to missingness)
## Null Deviance: 1.189e+09
## Residual Deviance: 1.122e+09 AIC: 80830summ(glm(formula = Reaction.Time ~ bdi.tot + stai.y1.tot + Target.WT,
data = sl.regression))| Observations | 5356 (99 missing obs. deleted) |
| Dependent variable | Reaction.Time |
| Type | Linear regression |
| 𝛘²(4) | 67289551.40 |
| Pseudo-R² (Cragg-Uhler) | 0.06 |
| Pseudo-R² (McFadden) | 0.00 |
| AIC | 80834.45 |
| BIC | 80873.97 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 1183.13 | 28.81 | 41.06 | 0.00 |
| bdi.tot | 4.17 | 0.85 | 4.94 | 0.00 |
| stai.y1.tot | 4.04 | 0.78 | 5.16 | 0.00 |
| Target.WTEmotion-Laden | 13.03 | 14.50 | 0.90 | 0.37 |
| Target.WTNeutral | 220.38 | 16.09 | 13.70 | 0.00 |
| Standard errors: MLE |
16 Graphics
#----
rm.exn.ps<-data.frame(Primed.Satiated =c("Primed","Satiated"),
Mean =c(1374.988,1419.129),
SE =c(34.774,34.774),
Lower =c(1305.742,1349.882),
Upper =c(1444.234,1488.375))
ggplot(rm.exn.ps, aes(x= Primed.Satiated, y = Mean)) +
geom_point(size=3, shape=20)+
geom_errorbar(aes(ymin = Lower, ymax = Upper), size=.5, width=.13) +
ylab("Mean Reaction Time (ms)")+
expand_limits(y = 0:2000) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Mean Reaction Time by Primed/Satiated")+
theme_grey()+
theme(rect = element_rect(fill = "transparent"))+
theme(text = element_text(size=20),axis.text.x = element_text(angle=20, hjust=1))
#----
rm.exn.wt.ps.val<-data.frame(Word.Type =c(rep("Emotion-label",4),rep("Emotion-laden",4)),
Primed.Satiated =c(rep(c("Primed","Primed","Satiated","Satiated"),2)),
Participant.Group =c(rep(c("Negative","Positive"),4)),
Mean =c(1359.033,1368.900,1425.369,1391.637,1426.055,1345.964,1387.767,1471.741),
SD =c(275.526,282.975,305.962,374.682,300.152,288.812,287.703,331.938)) %>%
mutate(., SE=.$SD/sqrt(35)) %>%
mutate(., Upper=.$Mean+.$SE,
Lower=.$Mean-.$SE)
ggplot(rm.exn.wt.ps.val, aes(x= Word.Type, y = Mean, group=Primed.Satiated, color=Primed.Satiated)) +
geom_point(size=3, shape=20, position=position_dodge(.2))+
geom_errorbar(aes(ymin = Lower, ymax = Upper), size=.5, width=.13, position=position_dodge(.2)) +
geom_line(position=position_dodge(.2))+
facet_grid(cols= vars(Participant.Group)) +
ylab("Mean Reaction Time (ms)")+
expand_limits(y = 0:2000) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Mean Reaction Time by Word Type & Primed/Satiated")+
theme_grey()+
theme(legend.title=element_blank())+
theme(rect = element_rect(fill = "transparent"))+
theme(text = element_text(size=20),axis.text.x = element_text(angle=20, hjust=1))
#----
rm.exn.bdi.ps<-data.frame(Primed.Satiated =c("Primed","Satiated"),
Mean =c(1373.709,1420.407),
SE =c(35.029,35.029),
Lower =c(1303.953,1350.650),
Upper =c(1443.466,1490.164))
ggplot(rm.exn.bdi.ps, aes(x= Primed.Satiated, y = Mean)) +
geom_point(size=3, shape=20)+
geom_errorbar(aes(ymin = Lower, ymax = Upper), size=.5, width=.13) +
ylab("Mean Reaction Time (ms)")+
expand_limits(y = 0:2000) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Mean Reaction Time by Primed/Satiated")+
theme_grey()+
theme(rect = element_rect(fill = "transparent"))+
theme(text = element_text(size=20),axis.text.x = element_text(angle=20, hjust=1))
#----
rm.exn.bdi.wt.ps.val<-data.frame(Word.Type =c(rep("Emotion-label",4),rep("Emotion-laden",4)),
Primed.Satiated =c(rep(c("Primed","Primed","Satiated","Satiated"),2)),
Participant.Group =c(rep(c("Negative","Positive"),4)),
Mean =c(1359.033,1368.900,1425.369,1391.637,1426.055,1345.964,1387.767,1471.741),
SD =c(275.526,282.975,305.962,374.682,300.152,288.812,287.703,331.938)) %>%
mutate(., SE=.$SD/sqrt(35)) %>%
mutate(., Upper=.$Mean+.$SE,
Lower=.$Mean-.$SE)
ggplot(rm.exn.bdi.wt.ps.val, aes(x= Word.Type, y = Mean, group=Primed.Satiated, color=Primed.Satiated)) +
geom_point(size=3, shape=20, position=position_dodge(.2))+
geom_errorbar(aes(ymin = Lower, ymax = Upper), size=.5, width=.13, position=position_dodge(.2)) +
geom_line(position=position_dodge(.2))+
facet_grid(cols= vars(Participant.Group)) +
ylab("Mean Reaction Time (ms)")+
expand_limits(y = 0:2000) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Mean Reaction Time by Word Type & Primed/Satiated")+
theme_grey()+
theme(legend.title=element_blank())+
theme(rect = element_rect(fill = "transparent"))+
theme(text = element_text(size=20),axis.text.x = element_text(angle=20, hjust=1))
#----
rm.incn.wt<-data.frame(Word.Type =c("Emotion-label","Emotion-laden","Neutral"),
Mean =c(1386.235,1407.882,1585.602),
SE =c(34.363,34.363,34.363),
Lower =c(1318.069,1339.716,1517.436),
Upper =c(1454.401,1476.048,1653.768))
ggplot(rm.incn.wt, aes(x= Word.Type, y = Mean)) +
geom_point(size=3, shape=20)+
geom_errorbar(aes(ymin = Lower, ymax = Upper), size=.5, width=.13) +
ylab("Mean Reaction Time (ms)")+
expand_limits(y = 0:2000) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Mean Reaction Time by Word Type")+
theme_grey()+
theme(rect = element_rect(fill = "transparent"))+
theme(text = element_text(size=20),axis.text.x = element_text(angle=20, hjust=1))
#----
rm.incn.wt.ps<-data.frame(Word.Type =c("Emotion-label","Emotion-label","Emotion-laden","Emotion-laden","Neutral","Neutral"),
Primed.Satiated =c(rep(c("Primed","Satiated"),3)),
Mean =c(1363.966,1408.503,1386.010,1429.754,1607.085,1564.119),
SE =c(36.394,36.394,36.394,36.394,36.394,36.394),
Lower =c(1291.946,1336.482,1313.989,1357.733,1535.064,1492.098),
Upper =c(1435.987,1480.524,1458.030,1501.775,1679.106,1636.140))
ggplot(rm.incn.wt.ps, aes(x= Word.Type, y = Mean, group=Primed.Satiated, color=Primed.Satiated)) +
geom_point(size=3, shape=20, position=position_dodge(.2))+
geom_errorbar(aes(ymin = Lower, ymax = Upper), size=.5, width=.13, position=position_dodge(.2)) +
geom_line(position=position_dodge(.2))+
ylab("Mean Reaction Time (ms)")+
expand_limits(y = 0:2000) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Mean Reaction Time by Word Type & Primed/Satiated")+
theme_grey()+
theme(legend.title=element_blank())+
theme(rect = element_rect(fill = "transparent"))+
theme(text = element_text(size=20),axis.text.x = element_text(angle=20, hjust=1))
#----
rm.incn.wt.ps.val<-data.frame(Word.Type =c(rep("Emotion-label",4),rep("Emotion-laden",4),rep("Neutral",4)),
Primed.Satiated =c(rep(c("Primed","Primed","Satiated","Satiated"),3)),
Participant.Group =c(rep(c("Negative","Positive"),6)),
Mean =c(1359.033,1368.900,1425.369,1391.637,1426.055,1345.964,1387.767,1471.741,1635.989,1578.180,1595.875,1532.362),
SD =c(275.526,282.975,305.962,374.682,300.152,288.812,287.703,331.938,266.254,332.288,244.566,339.172)) %>%
mutate(., SE=.$SD/sqrt(35)) %>%
mutate(., Upper=.$Mean+.$SE,
Lower=.$Mean-.$SE)
ggplot(rm.incn.wt.ps.val, aes(x= Word.Type, y = Mean, group=Primed.Satiated,color=Primed.Satiated)) +
geom_point(size=3, shape=20, position=position_dodge(.2))+
geom_errorbar(aes(ymin = Lower, ymax = Upper), size=.5, width=.13, position=position_dodge(.2)) +
geom_line(position=position_dodge(.2))+
facet_grid(cols= vars(Participant.Group)) +
ylab("Mean Reaction Time (ms)")+
expand_limits(y = 0:2000) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Mean Reaction Time by Word Type & Primed/Satiated")+
theme_grey()+
theme(legend.title=element_blank())+
theme(rect = element_rect(fill = "transparent"))+
theme(text = element_text(size=20),axis.text.x = element_text(angle=20, hjust=1))
#----
rm.incn.bdi.wt<-data.frame(Word.Type =c("Emotion-label","Emotion-laden","Neutral"),
Mean =c(1381.240,1406.022,1592.456),
SE =c(34.926,34.926,34.926),
Lower =c(1312.003,1336.785,1523.219),
Upper =c(1450.477,1475.260,1661.693))
ggplot(rm.incn.bdi.wt, aes(x= Word.Type, y = Mean)) +
geom_point(size=3, shape=20)+
geom_errorbar(aes(ymin = Lower, ymax = Upper), size=.5, width=.13) +
ylab("Mean Reaction Time (ms)")+
expand_limits(y = 0:2000) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Mean Reaction Time by Word Type")+
theme_grey()+
theme(rect = element_rect(fill = "transparent"))+
theme(text = element_text(size=20),axis.text.x = element_text(angle=20, hjust=1))
#----
rm.incn.bdi.wt.ps<-data.frame(Word.Type =c("Emotion-label","Emotion-label","Emotion-laden","Emotion-laden","Neutral","Neutral"),
Primed.Satiated =c(rep(c("Primed","Satiated"),3)),
Mean =c(1356.469,1406.011,1384.096,1427.949,1616.490,1568.423),
SE =c(37.409,37.409,37.409,37.409,37.409,37.409),
Lower =c(1282.504,1332.046,1310.131,1353.984,1542.525,1494.458),
Upper =c(1430.433,1479.976,1458.060,1501.913,1690.454,1642.387))
ggplot(rm.incn.bdi.wt.ps, aes(x= Word.Type, y = Mean, group=Primed.Satiated, color=Primed.Satiated)) +
geom_point(size=3, shape=20, position=position_dodge(.2))+
geom_errorbar(aes(ymin = Lower, ymax = Upper), size=.5, width=.13, position=position_dodge(.2)) +
geom_line(position=position_dodge(.2))+
ylab("Mean Reaction Time (ms)")+
expand_limits(y = 0:2000) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Mean Reaction Time by Word Type & Primed/Satiated")+
theme_grey()+
theme(legend.title=element_blank())+
theme(rect = element_rect(fill = "transparent"))+
theme(text = element_text(size=20),axis.text.x = element_text(angle=20, hjust=1))
#----
rm.incn.bdi.wt.ps.val<-data.frame(Word.Type =c(rep("Emotion-label",4),rep("Emotion-laden",4),rep("Neutral",4)),
Primed.Satiated =c(rep(c("Primed","Primed","Satiated","Satiated"),3)),
Participant.Group =c(rep(c("Negative","Positive"),6)),
Mean =c(1359.033,1368.900,1425.369,1391.637,1426.055,1345.964,1387.767,1471.741,1635.989,1578.180,1595.875,1532.362),
SD =c(275.526,282.975,305.962,374.682,300.152,288.812,287.703,331.938,266.254,332.288,244.566,339.172)) %>%
mutate(., SE=.$SD/sqrt(35)) %>%
mutate(., Upper=.$Mean+.$SE,
Lower=.$Mean-.$SE)
ggplot(rm.incn.bdi.wt.ps.val, aes(x= Word.Type, y = Mean, group=Primed.Satiated, color=Primed.Satiated)) +
geom_point(size=3, shape=20, position=position_dodge(.2))+
geom_errorbar(aes(ymin = Lower, ymax = Upper), size=.5, width=.13, position=position_dodge(.2)) +
geom_line(position=position_dodge(.2))+
facet_grid(cols= vars(Participant.Group)) +
ylab("Mean Reaction Time (ms)")+
expand_limits(y = 0:2000) +
scale_y_continuous(expand = c(0, 0)) +
ggtitle("Mean Reaction Time by Word Type & Primed/Satiated")+
theme_grey()+
theme(legend.title=element_blank())+
theme(rect = element_rect(fill = "transparent"))+
theme(text = element_text(size=20),axis.text.x = element_text(angle=20, hjust=1))
#scratch
#sl.factorized <- sl.trimmed %>%
# mutate_if(sapply(., is.character), as.factor) %>%
# mutate_if(sapply(., is.factor), fac.to.num)
#sl.agg.desc <- sl.factorized %>%
# dplyr::select(., Session.ID, Participant.Group, X.Primed.Satiated, Target.V, Target.WT, TPRValence, bdi, Error.Code, Reaction.Time) %>%
# group_by(., Session.ID, Participant.Group, X.Primed.Satiated, Target.V, TPRValence, Target.WT, bdi, Error.Code) %>%
# summarise_all(list(length = length, mean = mean, median = median, sd = sd, min = min, max = max)) %>%
# rename(RTCMean = "mean", n = "length", Congruency = TPRValence)
# sl.agg <- sl.factorized %>%
# dplyr::select(., Session.ID, Participant.Group, X.Primed.Satiated, Target.V, Target.WT, TPRValence, bdi, Error.Code, Reaction.Time) %>%
# group_by(., Session.ID, Participant.Group, X.Primed.Satiated, Target.V, TPRValence, Target.WT, bdi, Error.Code) %>%
# summarise_all(list(mean = mean)) %>%
# rename(RTCMean = mean, Congruency = TPRValence)
#
#sl.agg.rm <- sl.factorized %>%
# dplyr::select(Session.ID, Participant.Group, X.Primed.Satiated, Target.V, Target.WT, TPRValence, bdi, Error.Code, Reaction.Time) %>%
# group_by(Session.ID, Participant.Group, X.Primed.Satiated, Target.V, TPRValence, Target.WT, bdi, Error.Code) %>%
# summarise_all(list(mean = mean)) %>%
# filter(Error.Code == 1) %>%
# merge(., c(distinct(sl.agg[, 1:2])), by = "Session.ID") %>%
# dplyr::select(., -Participant.Group.y) %>%
# rename(RTCMean = mean, Congruency = TPRValence, Participant.Group = Participant.Group.x) %>%
# mutate(Participant.Group = ifelse(.$Participant.Group %in% c(1, 2), 1, 2))
#
# sl.agg.rm.neutral.spss <- sl.agg.rm %>%
# recast(.,
# formula = Session.ID ~ Target.WT + X.Primed.Satiated,
# id.var = c("Session.ID", "Target.WT", "X.Primed.Satiated"),
# measure.var = "RTCMean",
# fun = mean
# ) %>%
# merge(., c(distinct(sl.agg[, c(1:2, 7)])), by = "Session.ID") %>%
# mutate(Participant.Group = ifelse(.$Participant.Group %in% c(1, 2), 1, 2))
# write.csv(sl.agg.rm.neutral.spss, "sl.agg.rm.spss.neutral.csv")